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Computational Intelligence and Neuroscience

Volume 2008 (2008), Article ID 857453, 14 pages

http://dx.doi.org/10.1155/2008/857453

## Robust Object Recognition under Partial Occlusions Using NMF

Smart systems division, ARC Seibersdorf research GmbH, 2444 Seibersdorf, Austria

Received 2 October 2007; Revised 18 December 2007; Accepted 10 March 2008

Academic Editor: Morten Morup

Copyright © 2008 Daniel Soukup and Ivan Bajla. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

#### Abstract

In recent years, nonnegative matrix factorization (NMF) methods of a reduced image data representation attracted the attention of computer vision community. These methods are considered as a convenient part-based representation of image data for recognition tasks with occluded objects. A novel modification in NMF recognition tasks is proposed which utilizes the matrix sparseness control introduced by Hoyer. We have analyzed the influence of sparseness on recognition rates (RRs) for various dimensions of subspaces generated for two image databases, ORL face database, and USPS handwritten digit database. We have studied the behavior of four types of distances between a projected unknown image object and feature vectors in NMF subspaces generated for training data. One of these metrics also is a novelty we proposed. In the recognition phase, partial occlusions in the test images have been modeled by putting two randomly large, randomly positioned black rectangles into each test image.

#### 1. Introduction

Subspace methods represent a separate branch of high-dimensional data analysis, such as in areas of computer vision and pattern recognition. In particular, these methods have found efficient applications in the fields of face identification and recognition of digits and characters. In general, they are characterized by learning a set of basis vectors from a set of suitable image templates. The subspace spanned by this vector basis captures the essential structure of the input data. Having found the subspace (offline phase), the classification of a new image (online phase) is accomplished by projecting it on the subspace in some way and by finding the nearest neighbor of templates projected onto this subspace.

In 1999, Lee and Seung [1] showed for the first time that for a collection of face images an approximative representation by basis vectors, encoding the mouth, nose, and eyes, can be obtained using a nonnegative matrix factorization (NMF). NMF is a method for generating a linear representation of data using nonnegativity constraints on the basis vector components and the coefficients. It can formally be described as follows: where is a positive image data matrix with pixels and image sample templates (template images are usually represented in lexicographic order of pixels as column vectors), are reduced basis column vectors of an NMF subspace, and contains coefficients of the linear combinations of the basis vectors needed to reconstruct the original data. Usually, is chosen by the user so that . Then each column of the matrix represents a basis vector of the generated (NMF)subspace. Each column of represents the weights needed to approximate the corresponding column in (image template) by means of the vector basis . Various error functions were proposed for NMF, such as in the papers of Lee and Seung [2] or Paatero and Tapper [3].

The main idea of NMF application in visual object recognition is that the NMF algorithm identifies localized parts describing the structure of that object type. These localized parts can be added in a purely additive way with varying combination coefficients to form the individual objects. The original algorithm of Lee and Seung could not achieve this locality of essential object parts in a proper way. Thus other authors investigated the possibilities to control the sparseness of the basis images (columns in ) and the coefficients (matrix ). The first attempts consisted in altering the norm that measures the approximation accuracy, like LNMF [4, 5]. Hoyer introduced a method for steering the sparsenesses of both factor matrices, and , with two sparseness parameters [6, 7]. In their work, Pascual-Montano et al. briefly summarized and described all NMF algorithms used in this topic [8]. Their approach also led to a sparseness control parameter, but only one for both matrices. The optimization algorithm remained equal to the one already introduced by Lee and Seung.

One important problem by using NMF for recognition tasks is how to obtain NMF subspace projections for new image data that are comparable with the feature vectors determined in NMF coded in matrix . Guillamet and Vitrià [9] propose one method in their work that consists of rerunning the NMF algorithm for new image data keeping constant. However, in the conventional method, training images and new images are orthogonally projected onto the determined subspace. Both methods have advantages and drawbacks. We will discuss them in more detail and propose a modification of the NMF task that comprises the advantages of both methods.

An important aspect in measuring distances in NMF subspaces, which is necessary in recognition tasks, is the used metric. NMF subspace basis vectors do not form an orthogonal system. Due to this fact, it is not convenient to apply the natural Euclidean metric. Guillamet and Vitrià [9] experimented with several alternative metrics: , , cos, and EMD. They lined out that solely EMD takes the positive aspects of NMF into account. As this metric is computationally demanding, Ling and Okada [10] proposed a new dissimilarity measure, the diffusion concept, which is as accurate as EMD, but computationally much more efficient. Liu et al. [11, 12] proposed to replace the Euclidean distance in NMF recognition tasks by a weighted Euclidean distance (a version of Riemannian distance). These authors also experimented with orthogonalized bases. However, as commented by authors, these modified NMF bases are not part-based anymore.

In our research, we focus on studying the influence of matrix sparseness parameters, subspace dimension, and the use of distance measures on the recognition rates, in particular for partially occluded objects. We use Hoyer's algorithms to achieve sparseness control. Additionally, we propose a modification of the entire NMF task similar to the methods of Yuan and Oja [13] and Ding et al. [14]. The implementation of our modification additionally comprises Hoyer's sparseness control mechanisms. In the case of studying proper distance measures, we propose a new metric.

In Section 2, we briefly review Hoyer's method (Section 2.1). Section 2.2 contains a presentation of the motivation and a detailed description of our modification of the NMF task. Section 2.3 is about distance measuring in NMF subspaces. We present the metrics we used for our experiments and propose the anew distance measure. Then we present the setup and results of our experiments in Section 3. Section 4 contains conclusions and a future outlook.

#### 2. Nmf with Sparseness Constraints

The aim of the work of Hoyer [7] is to constrain NMF to find a solution with prescribed degrees of sparseness of the matrices and . The author claims that the balance of the sparseness between these two matrices depends on the specific application and no general recommendation can be given. The modified NMF problem and its solution is given by Hoyer as follows.

##### 2.1. Hoyer's Method—Nmfsc

###### 2.1.1. Problem Definition

Given a nonnegative
data matrix of size , find the nonnegative matrices and of sizes and (resp.,) such
thatis minimized, under optional
constraints
where is the th
*column of *, is the row
of . Here, denotes the dimensionality of an NMF
subspace spanned by the column vectors of the matrix
, and
and
are their
desired sparseness values. The sparseness criteria proposed by Hoyer [7] use a measure based on the
relationship between
and
norm of the
given vectors
or
. In general, for the give -dimensional vector
with the components
, its sparseness measure is defined by the
formula:
This measure
quantifies how much energy of the vector is packed into a few components. This
function evaluates to 1 if and only if the given vector contains a single
nonzero component. Its value is 0 if and only if all components are equal. It
should be noted that the scales of the vectors or have not been
constrained yet. However, since we are free to arbitrarily fix any norm of either
one. In Hoyer's algorithm, the norm of is fixed to
unity.

###### 2.1.2. Factorization Algorithm

The projected gradient descent algorithm for NMF with sparseness constraints essentially takes a step in the direction of the negative gradient, and subsequently projects onto the constraint space, making sure that the taken step is small enough that the objective function is reduced at every step. The main muscle of the algorithm is the projection operator proposed by Hoyer [7], which enforces the required degree of sparseness.

##### 2.2. Modified NMF Concept: ModNMF

In the papers mentioned up to now, the attention was concentrated on methodological aspects of NMF as a part-based representation of image data, as well as on numerical properties of the developed optimization algorithms applied to the matrix factorization problem. It turned out that the notion of matrix sparseness involved in NMF plays the central role in part-based representation. However, little effort has been devoted to systematic analysis of the behavior of the NMF algorithms in actual pattern recognition problems, especially for partially occluded data.

For a particular recognition, task of objects
represented by a set of training images () we need:
(i) to calculate in advance (in an offline mode) projection vectors of the
training images onto the obtained vector basis ()—the
so-called feature vectors—, and then (ii) to calculate (in an online mode) a
projection vector onto the obtained vector basis () for each
unknown input vector . Guillamet and Vitrià [9] propose to use the feature vectors determined in the
NMF run, that is, columns of matrix . The problem of determining projected vectors for new
input vectors in a way that they are comparable with the feature vectors is
solved by the authors by rerunning the NMF algorithm. In this second run, they
keep the basis matrix constant and
the matrix contains the
new input vectors instead of the training image vectors. The results of the
second run are the searched projected vectors in the matrix . However, this method has some drawbacks. We
investigated the function of NMF exemplarily for 3D point data instead of
high-dimensional images. These points have been divided into two classes based
on point proximity. The two classes are called
**A** and **B**
and are illustrated in
Figure 1. We ran NMF to get a two dimensional subspace visualized as yellow
grid in Figure 1 spanned by the two vectors and , which together build matrix . Additionally, we show the feature vectors of the
input point sets ( and in Figure 1)
and connected each input point with its corresponding feature vector in the
subspace plane (projection rays). Especially for the point set , it can be
observed that the projection rays are all nonorthogonal, with respect to, the
plane and that their mutual angles significantly differ (even for feature
vectors belonging to the same class). Thus the feature vectors of set and set
are not separated clusters anymore. We have doubts that a reliable
classification based on proximity of feature vectors is achievable in this
case. A second possibility to determine proper feature vectors for an NMF
subspace, which is conventionally used (e.g., mentioned by Buciu [15]), is to recompute the
training feature vectors for the classification phase entirely new by
orthogonally projecting the training points (images) onto to NMF subspace. Unknown
input data to be classified are similarly orthogonally projected to the
subspace. This method is also visualized in Figure 1: from each input point an
orthogonal dotted line is drawn to the orthogonal projections of the points
into the subspace plane. It can be noticed that the feature vectors determined
in this way preserve a separation of the feature vector clusters, corresponding
to the cluster separation in the original data space (point sets and ). In view of
these observations, we propose to favor the orthogonal projection method.

Nonetheless, both methods have their disadvantages. The method of Guillamet and Vitria operates with nonorthogonal projected feature vectors that directly stem from the NMF algorithm and do not reflect the data cluster separation in the subspace. On the other hand, the conventional method does not accommodate the optimal data approximation result determined in NMF because one of the two optimal factor matrices is substituted by a different one in the classification phase. Our intention was to combine the benefits of both methods into one, that is, benefits of orthogonal projections of input data and preservation of the optimal training data approximation of NMF. We achieve this by changing the NMF task itself. Before we present this modification, we recall in more detail how the orthogonal projections of the input data are computed.

As the basis matrix is rectangular, matrix inversion is not defined. Therefore, one has to use a pseudo-inverse of to multiply it from the left onto (cf. [15]). Orthogonal projections of data points onto a subspace defined by a basis vector matrix are realized by solving the following overdetermined equation system: for the coefficient vector . This can, for instance, be achieved via the Moore-Penrose (M-P) pseudoinverse (This may not be the numerically stable way, but in our investigations we could not observe differences to other usually more appropriate methods.) giving the result for the projection asSimilarly, for the NMF feature vectors (in the offline mode) we determine , where are projection coefficients obtained in the least squares (LS) manner. These coefficients can differ severely from the NMF feature vectors implicitly given by (see Figure 1). It is important to state that the entries of are not nonnegative anymore, also contains negative values.

If one has decided to use the orthogonal projections of input data onto the subspace as feature vectors, the fact that the matrix is not used anymore in the classification phase and that the used substitute for —is not nonnegative anymore, gives rise to the questions whether matrix is necessary at all in NMF and whether corresponding coefficient coding necessarily has to be nonnegative. Moreover, using the orthogonal projection method, we do not make use of the optimal factorization achieved by NMF, as the coefficient matrix is altered for classification. Consequently, we propose the following modification of the NMF task itself:

*given the training matrix *, *we search for a matrix ** such that*

Within this novel concept (*modNMF*), is updated in
the same way as in common NMF algorithms. Even the sparseness of can be
controlled by the standard mechanisms, for example, those of Hoyer's method.
Only the coding matrix is substituted
by the matrix to determine
the current approximation error. Thus this new concept can be applied to all
existing NMF algorithms. In our research, we implemented and analyzed modNMF
comprising the sparseness control mechanisms of Hoyer.

There are two existing methods that are related to
modNMF in two complementary ways. In projective NMF (*pNMF*) of Yuan and
Oja [13], the
independent factor matrix is given up
and, similarly to modNMF, substituted by a coding matrix derived from and , namely, . Ding et al. [14] realize the second change
incorporated in modNMF. They keep two independent factor matrices in their *semi-NMF* method, but give up the nonnegativity restriction for one of them. Unlike
modNMF, the nonnegativity constraint is kept for the coding matrix while the signs
in the subspace basis are not
restricted. Following Ding et al. notion, modNMF is also only
semi-nonnegative. The resulting subspaces of semi-NMF and modNMF are not
classical NMF subspaces anymore. However, in the traditional NMF methods, as we
have outlined above, the training images have to be orthogonally projected to
the determined subspace in preparation of the object recognition phase and this
also results in mixed sign subspace vectors. (Due to this very fact that for
traditional NMF methods as well as for modNMF the actually used subspace
features in the recognition phase are not purely nonnegative and to the fact
that the determined subspace bases are all nonorthogonal in general, we face
the same problems in the recognition phase for modNMF and classical NMF
subspaces. To simplify matters in this paper, we summarize all these subspaces
in the notion *NMF subspaces* in our further discussions, which address
the issues related to recognition experiments in such
subspaces.) The
difference in the case of modNMF is that the orthogonal projections of the
training images onto the subspace that are used in the recognition task () are also
those for which the factorization that optimally approximates the training data is achieved. In
semi-NMF, this is not guaranteed, that is, extra orthogonal projection of the
training images onto the subspace has to be done to prepare an object
recognition phase. These extra projections do not comprise the structure of the
optimally approximating factor matrix determined in the factorization run, just
like in classical NMF methods. Similarly to modNMF, pNMF assures that the
subspace features are the orthogonal projections of the training images onto
the subspace, while these very subspace features simultaneously constitute the
optimal factorization matrix in the sense of approximating . Actually, pNMF is in some sense a special case of
modNMF. Both try to optimize with the goal
to approximate an identity matrix as close as possible in form of the factor in
front of -modNMF in the case of and pNMF for . Thus although orthogonality of in pNMF may not
be explicitly demanded, within the factorization process, has to
approximate an orthogonal matrix more and more as the approximation improves.
Thanks to the fact that the more general modNMF model does not contain such
structural restrictions on (except
nonnegativity), there are more degrees of freedom in modNMF to approximate accurately.
Moreover, the sparseness of can be
controlled in modNMF via the sparseness parameter.

##### 2.3. Distances in NMF Subspaces

Having solved the NMF task for the given training images (matrix ), the vector basis of an NMF subspace (of the original data space) is generated as columns of the matrix . Depending on the sparseness of and controlled in the algorithms, the basis vectors in manifest different mutual angles, that is, the basis is not orthogonal. With increasing sparseness of or decreasing sparseness of , the mutual angles tend to be closer to orthogonality. If both sparseness parameters are adjusted, dependence on them is not so obvious and straightforward.

As outlined by various authors mentioned in Section 1,
suitable metrics for measuring the distances of NMF subspace points have to be
defined, due to the non-orthogonality of NMF subspace bases. In our work, we
compared the four metrics *Euclidean*, *diffusion*, *Riemannian*,
and *ARC-distance*.

For comparison reasons, we also included the Euclidean metric , which is commonly supposed not to be suitable in vector spaces with nonorthogonal basis. The diffusion distance is derived from the EMD metric, for which Guillamet and Vitrià [9] argued that it is well suited to the positive aspects of NMF. The complete derivative of the diffusion distance can be found in the work of Ling and Okada, who developed this dissimilarity concept to achieve a computationally more efficient algorithm.

The third metric, Riemannian distance, will be described in more detail, as it is the basis of our proposal, ARC-distance. Liu and Zheng [11] defined the Riemannian distance as a weighted Euclidean distance as , where is a similarity matrix defined as . They claimed that adopting this Riemannian metric is more suitable than the Euclidean distance for classification when using nearest neighbor classifiers.

For the standard Euclidean metric and Riemannian metric of two vectors from a subspace, the following formulas can be drawn: . This proves that the Riemannian distance measures the Euclidean distance of the back-projected subspace vectors, that is, the subspace points represented in the orthogonal image super space bases. Thus the Riemannian distance takes the angle structure of the NMF subspace bases into account.

To be able to deal with partial occlusions, the correctly chosen distance measure should also be able to discriminate two specific cases of vectors: (i) a case for which the value of the Riemannian distance of two vectors is large because of great deviations in all components of these vectors, and (ii) a case when only a few components contribute to the great value of the Riemannian distance, that is, when the error of recognition is sparsely distributed over the feature vector components. Therefore, to define a modified Riemannian (shortly “ARC-distance”) distance, we introduce a sparseness term into the Riemannian metric formula, that is, where measures the sparseness (compare Section 2) of the absolute difference of the feature vectors. Note that the sparseness should be measured in the feature space, as each component in this space representation is optimized to reflect one essential part of the training image objects.

#### 3. Results of Computer Experiments

The goal of our study was to investigate influences of *sparseness control parameters* and *subspace metrics* on recognition
rates of unoccluded and occluded images. In massive computer experiments, we
have varied the dimensions of the NMF subspaces from up to , similarly to the papers of Guillamet and Vitrià
[9] and Liu and Zheng
[11]. The method of *nearest
neighbor classification* has been used for object recognition.

For our experiments, we chose three widely used image databases: (i) the Cambridge ORL face database (cited in paper of Li et al. [5]; grey-level images with resolution , which were down sampled for our experiments to the size pixels) and (ii) USPS handwritten digit database (cited in the paper of Liu and Zheng [11]; grey-level images with resolution pixels), and (iii) CBCL image database available at the web address: http://cbcl.mit.edu/cbcl/software-datasets/FaceData2.html (cited in the paper of Hoyer [7]) that contains grey-level face images with resolution pixels. We simulated object occlusions in test images as two rectangles of random (but limited) sizes with random super positioning on an original image (see Figures 2, 3, and 4).

In the case of ORL database, the number of training
images was 222, and the number of testing images was 151. These two sets of
images were chosen as disjunctive sets. For the experiments with USPS database,
we chose 2000 training images and 1000 testing images (different from the
training ones again). (In the USPS
recognition rate plots (Figures 6, 8), data points for are missing.
This is due to a Matlab problem that could not be solved. For some reason, all
subspace files containing matrix
**H**
with dimension were corrupted
and could not be opened anymore. We were able to reproduce the error in
simplified configurations, however, we were not able to solve it. As the
recognition curves do not oscillate a lot, we found it justified to just
interpolate between the two neighboring points of the point in .) For the case of the CBCL image database, we used
1620 training images and 809 testing images.

##### 3.1. Nmfsc—Unoccluded versus Occluded Test Images

The results of the first set of our experiments, accomplished for all three image bases, and for unoccluded, as well as occluded images are displayed in Figures 5, 6, and 7. The acronym “Nmfsc” stands here for Hoyer's NMF method with coded sparseness . In this set of tests, Hoyer's Nmfsc-algorithm was applied consecutively to ORL face images, USPS digits, and CBCL face images. The algorithms have been trained for various combinations of sparseness parameter values. The resulting NMF subspaces, calculated for different dimensions were used for recognition experiments. We used four types of distances to measure the distance of each projected test image to the nearest feature vector (of the templates) in the given subspace. For each NMF subspace, a recognition rate (RR) over all test images was calculated. The plots show RR versus subspace dimension (unoccluded—(a), (c), (e), and occluded—(b), (d), (f)). The plots with the best recognition results have been chosen.

For unoccluded images, all three data sets show similar RR behavior in the cases of the Riemannian-like metrics (Riemannian and ARC-distance), only CBCL RR are slightly smaller. The Euclidean and diffusion curves for the ORL and CBCL data are almost as high as for the Riemannian-like measures, but also, as one would expect. Their behavior for USPS data even more fulfills these expectations, as they are much smaller than the Riemannian-like RR curves and, moreover, decrease with increasing dimension. This behavior is expectable, as more (nonorthogonal) basis vectors introduce more error components into the distance computation. This happens due to the fact that Euclidean and diffusion distance do not take into account the mutual basis vector angles. The dimension reduction for all datasets is very high, as for Riemannian-like metric all three achieve the maximal RR at about . Remarkable is that ARC-distance does not differ from Riemannian distance. It can be seen (Figures 7(a), 7(c), 7(e)) that the RR values for all types of distances are lower (below 0.9) than those achieved for ORL faces. There are only small differences in RR between the cases corresponding to application of different distances, but in general, Riemannian distance yields the maximum values.

The RR behavior for occluded data differs severely between ORL and USPS data. First, RR maxima for USPS data are higher than for ORL data—below 0.7 in the ORL case versus about 0.75 for USPS data. Second, for ORL data the RR curves of the metrics do not behave in the expected way. Euclidean and diffusion distance generate much better results than the Riemannian-like. For USPS, RR behave qualitatively in the same way as in the unoccluded case, RR values are only smaller. Finally, RR maxima are achieved for higher dimension values in the ORL case, that is, a much smaller dimension reduction. In the case of CBCL image database, the situation changes dramatically in comparison to that of ORL face images: in average the RR are 50% smaller, they are reaching approximately the value of 0.3 (comparing to 0.7 maximum for ORL). For two value combinations of the sparseness parameters in Hoyer's method (Figures 7(b), 7(f)) the Euclidean distance yields higher RR, though it is not strictly monotone; however, for the case D, the Riemannian distance outperforms Euclidean and diffusion ones. The difference between RR values for Riemannian distances on one side, and Euclidean and diffusion distances on the other side are apparent but not so large as is the case of ORL face images.

##### 3.2. Occluded Test Images—Nmfsc versus ModNMF

In the second part of our study, we were interested in a comparison of the RR of Nmfsc and modNMF, latter one being implemented with Hoyer's sparseness control mechanisms. Of course, since the NMF methodology is intended mainly to generate part-based subspace representation of template images, our further interest was concentrated only on occluded images. These results, obtained for optimum values of sparseness parameter , are displayed in Figures 8, 9, and 10. The plots also show RR versus subspace dimension but the columns now discriminate the used algorithms (Nmfsc—(a), (c), (e), and modNMF—(b), (d), (f)). The plots with the best recognition results have been chosen.

The qualitative behavior of the RR curves of ORL faces according to the distance measures is the same as described in Section 3.1. Euclidean and diffusion distances unexpectedly dominate the riemannian-like metrics. Except a break-in of RR values for the Euclidean and diffusion distances in the case of Nmfsc with , both algorithms, Nmfsc and modNMF achieve approximately the same results (Figure 8). The qualitatively more expected and quantitatively better results (w.r.t., RR maxima) are obtained in the case of the USPS data. For Nmfsc with only the parameter set, the Riemannian-like RR curves dominate the Euclidean and diffusion distances, whereas—as expectable—the latter decrease with increasing dimension and decreasing sparseness (Section 2.3). Remarkable is that the novel modNMF algorithm increases and stabilizes the performance of the Euclidean and diffusion distances. The plots show that the curves of these two metrics are close to the Riemannian-like ones. The CBCL image data comprise face images which have significantly lower spatial resolution than the face data in the ORL image base, while the structure of their parts is similarly complex. These characteristics are reflected in apparent decrease of recognition rates for occluded images for both methods being compared. In general, the behavior of the recognition rates manifests in this case very low sensitivity to the choice of the sparseness parameters. None of the distances applied exhibits unique prevalence.

#### 4. Conclusions

In this paper, we have analyzed the influence of the matrix sparseness, controlled in NMF tasks via Hoyer's algorithm [7], from the viewpoint of object recognition efficiency. A special interest was devoted to partially occluded images, since images without occlusions can similarly well be handled by all NMF methods. Besides, Hoyer's algorithm, we introduced a modified version of the NMF concept—modNMF—using a term containing the Moore-Penrose pseudoinverse of the basis matrix instead of the coefficient matrix . Among the discussed important theoretical advantages, this method provides the computational benefit that the subspace projections of the training images do not have to be calculated after subspace generation in an additional step. The novel concept was implemented comprising the sparseness modification mechanism of Nmfsc. A further goal of the paper was to analyze and compare RR achieved for four different metrics used in the recognition tasks. As NMF subspace bases are nonorthogonal, distance measuring is a crucial aspect. The computer experiments were accomplished for three different image databases, ORL, USPS, and CBCL. In the classification tasks, we used the nearest neighbor method. In the unoccluded cases, Riemannian-like distances dominate RR quality in maxima and stability over all subspace dimensions and all parameter settings. ORL and USPS only differ slightly in the behavior of Euclidean and diffusion distances. In the case of CBCL, small differences of RR are manifested between the cases using different distances. The conclusions related to the results for the occluded test images can be summarized as follows.

(1) The ability of NMF methods to solve recognition tasks is dependent on the kind of used images and the databases as a whole. Independently of the method, the RR for USPS data are higher than those for ORL face data. This finding could be ascribed to the simpler structure of the digits (almost binary data, lower resolution, objects sparsely cover the image area). Moreover, USPS contain much larger classes (USPS: 2000 training images for only 10 classes, ORL: 222 images with only 5 training images per class), so that the interclass variations in USPS can better be covered. In general, the RR obtained for faces from the CBCL database are significantly worse than in comparable cases with ORL face images. We assign these results to the poor resolution of the structured face image data.

(2) Not following the overall expectation, Euclidean and diffusion distances showed better recognition performances for occluded test images in the case of ORL data. As these do not take into account subspace bases angles this is a surprise. USPS data treated with Hoyer's Nmfsc method behave like expected: with increasing dimension and decreasing (i.e., increasing orthogonality, see Section 2.3), the RR measured with Euclidean and diffusion distances decrease (almost) monotonically. On the other hand, using our modNMF method, Euclidean and diffusion distances perform almost as well as the Riemannian-like metrics overall dimensions and sparseness values. This gives a hint that the relatively bad performances of these two metrics for the Nmfsc method cannot totally be ascribed to the nonorthogonality of the bases, but to the used orthogonal projections of the training images () instead of the well approximating factor matrix in the classification phase; since we have observed no differences between the RR for the original Riemannian distance and ARC-distance, the proposed formula will need further exploration, likely to introduce some kind of numerical emphasis of the added sparseness term, as for example, exponential.

(3) Massive recognition experiments using Nmsfc and modNMF algorithms, reported in our preliminary study [16], showed minor influence of sparseness parameter on recognition rates in cases of unoccluded, as well as occluded images selected from three mentioned image databases. Therefore, in the recognition experiments with occluded images included in this study, the sparseness parameter has not been controlled and we have been experimenting exclusively with sparseness value of the NMF basis matrix. Namely, we used three representative values: . As mentioned above we applied two NMF methods, conventional Nmsfc and our modified modNMF algorithm. Based on the analysis of the plots of RR for these methods and for images from three image databases, given in Figures 8, 9, and 10, the following conclusions on influence of the sparseness on RR can be drawn as follows:

(i)*ORL face
images*:
*Nmsfc method*: the maximum RR have been
achieved for , the minimum RR have been achieved for ; *modNMF method*: the maximum RR have been
obtained for , however, the values of RR for were close to
maxima; the minimum values of the RR have been obtained for ;(ii)*CBCL face
images*:
*Nmsfc method*: the maximum RR have been
achieved for , the minimum RR have been achieved for ; *modNMF method*: the maximum RR have been
obtained for , however the values of RR for were, similarly
to the case of ORL, also close to maxima; the minimum values of the RR have
been obtained for ;(iii)*USPS digit
images*: for both NMF methods
compared, there were no significant influence of the sparseness parameter on RR observed.

USPS performed better and followed the overall expectations better than ORL and CBCL. We basically ascribe this fact to the different training data situations. As mentioned in the first point above, inter-class variations were much more covered for the USPS dataset than for the face images. The novel modNMF algorithm even improved the results achieved in the case of the already well performing USPS data set. ARC-distance in its current form did not fulfill the expectations in the experiments. Significantly, lower spatial resolution of the CBCL face data than the face data in the ORL image base is reflected in apparent decrease of recognition rates for occluded images for both methods being compared. Various distances used for the CBCL database manifested little influence on RR.

Spratling [17] analyzed the methodological situation related to the concept of “part-based” representation of image data by NMF subspaces, and pointed on the weaknesses of application of this concept in the NMF framework. Inspired by Spratling's results, we have analyzed possibilities of further research of improvement of the NMF methodology using a revisited version of this concept that could be more attractive for object recognition tasks with occlusions. The research into this NMF version is in progress.

#### References

- D. D. Lee and H. S. Seung, “Learning the parts of objects by non-negative matrix factorization,”
*Nature*, vol. 401, no. 6755, pp. 788–791, 1999. View at Publisher · View at Google Scholar - D. D. Lee and H. S. Seung, “Algorithms for non-negative matrix factorization,” in
*Advances in Neural Information Processing Systems 13*, MIT Press, Cambridge, Mass, USA, 2001. - P. Paatero and U. Tapper, “Positive matrix factorization: a non-negative factor model with optimal utilization of error
estimates of data values,”
*Environmetrics*, vol. 5, no. 2, pp. 111–126, 1994. View at Publisher · View at Google Scholar - T. Feng, S. Z. Li, H.-Y. Shum, and H. Zhang, “Local non-negative matrix factorization as a visual representation,” in
*Proceedings of the 2nd International Conference on Development and Learning (ICDL '02)*, pp. 178–183, Cambridge, Mass, USA, June 2002. View at Publisher · View at Google Scholar - S. Z. Li, X. W. Hou, H. J. Zhang, and Q. S. Cheng, “Learning spatially localized, parts-based representation,” in
*Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '01)*, vol. 1, pp. 207–212, Kauai, Hawaii, USA, December 2001. View at Publisher · View at Google Scholar - P. O. Hoyer, “Non-negative sparse coding,” in
*Proceedings of the 12th IEEE Workshop on Neural Networks for Signal Processing (NNSP '02)*, pp. 557–565, Martigny, Switzerland, September 2002. View at Publisher · View at Google Scholar - P. O. Hoyer, “Nonnegative matrix factorization with sparseness constraints,”
*Journal of Machine Learning Research*, vol. 5, pp. 1457–1469, 2004. - A. Pascual-Montano, J. M. Carazo, K. Kochi, D. Lehman, and R. D. Pascual-Marqui, “Nonsmooth non-negative matrix factorization (nsNMF),”
*IEEE Transactions on Pattern Analysis and Machine Intelligence*, vol. 28, no. 3, pp. 403–415, 2006. View at Publisher · View at Google Scholar - D. Guillamet and J. Vitrià, “Evaluation of distance metrics for recognition based on non-negative matrix factorization,”
*Pattern Recognition Letters*, vol. 24, no. 9-10, pp. 1599–1605, 2003. View at Publisher · View at Google Scholar - H. Ling and K. Okada, “Diffusion distance for histogram comparison,” in
*Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '06)*, vol. 1, pp. 246–253, New York, NY, USA, June 2006. View at Publisher · View at Google Scholar - W. Liu and N. Zheng, “Non-negative matrix factorization based methods for object recognition,”
*Pattern Recognition Letters*, vol. 25, no. 8, pp. 893–897, 2004. View at Publisher · View at Google Scholar - W. Liu, N. Zheng, and X. Lu, “Nonnegative matrix factorization for visual coding,” in
*Proceedings of the 2nd IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '03)*, vol. 3, pp. 293–296, Hong Kong, April 2003. View at Publisher · View at Google Scholar - Z. Yuan and E. Oja, “Projective nonnegative matrix factorization for image compression and feature extraction,” in
*Proceedings of the 14th Scandinavian Conference on Image Analysis (SCIA '05)*, pp. 333–342, Joensuu, Finland, June 2005. View at Publisher · View at Google Scholar - C. Ding, T. Li, and M. I. Jordan, “Convex and semi-nonnegative matrix factorizations,” Tech. Rep., Lawrence Berkeley National Laboratory, Berkeley, Calif, USA, 2006.
- I. Buciu, “Learning sparse non-negative features for object recogntion,” in
*Proceedings of the 3rd IEEE International Conference on Intelligent Computer Communication and Processing (ICCP '07)*, pp. 73–79, Cluj-Napoca, Romania, September 2007. - I. Bajla and D. Soukup, “Non-negative matrix factorization: a study on influence of matrix sparseness and subspace distance metrics on image object recognition,” in
*8th International Conference on Quality Control by Artificial Vision*, D. Fofi and F. Meriaudeau, Eds., vol. 6356 of*Proceedings of SPIE*, pp. 1–12, Le Creusot, France, May 2007. View at Publisher · View at Google Scholar - M. W. Spratling, “Learning image components for object recognition,”
*Journal of Machine Learning Research*, vol. 7, pp. 793–815, 2006.