Mathematical Problems in Engineering

Volume 2016 (2016), Article ID 8740593, 11 pages

http://dx.doi.org/10.1155/2016/8740593

## Graph-Based Salient Region Detection through Linear Neighborhoods

^{1}School of Computer Science and Technology, Dalian University of Technology, No. 2 Linggong Road, Dalian 116024, China^{2}School of Computer and Information Technology, Liaoning Normal University, No. 850 Huanghe Road, Dalian 116024, China

Received 17 March 2016; Accepted 9 May 2016

Academic Editor: Chanho Jung

Copyright © 2016 Lijuan Xu et al. 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

Pairwise neighboring relationships estimated by Gaussian weight function have been extensively adopted in the graph-based salient region detection methods recently. However, the learning of the parameters remains a problem as nonoptimal models will affect the detection results significantly. To tackle this challenge, we first apply the adjacent information provided by all neighbors of each node to construct the undirected weight graph, based on the assumption that every node can be optimally reconstructed by a linear combination of its neighbors. Then, the saliency detection is modeled as the process of graph labelling by learning from partially selected seeds (labeled data) in the graph. The promising experimental results presented on some datasets demonstrate the effectiveness and reliability of our proposed graph-based saliency detection method through linear neighborhoods.

#### 1. Introduction

The goal of saliency detection is to identify and locate the most interesting and important region that pops out from the rest in an image, which has been widely used for applications in computer vision, including object detection and recognition [1, 2], image compression [3], image segmentation [4], content based image retrieval [5], image cropping [6], and photo collage [7].

Numerous researches have been conducted to design various algorithms for salient region detection. Among these works, graph-based saliency detection models have aroused considerable interest in recent years. Previous works on detecting salient regions from images represented as graphs include [8–15]. These models describe the input image as an undirected weight graph, in which vertices represent the image elements (pixels/regions) and edges represent the pairwise dissimilarity between vertices, and the salient object detection problem is formulated as random walks [8–10], binary segmentation [11, 12], labelling (ranking) task [13, 14], or distance metric [15] on the graph, which aims at finding the pop-out vertices at some local or global locations.

In methods of the random walks on graphs [8–10], the identification of salient regions is determined by the frequency of visits to each node at equilibrium. In [8], while some results are presented on only two synthetic images, there is no evaluation of how the method will work on real images. In [9], Harel et al. constructed the full-connected directed graph to represent the image in which the weight of the edge between two vertices is proportional to their dissimilarity, as well as their closeness in the spatial domain. Nonsalient regions are defined as the most frequently visited vertices in a local context. Wang et al. [10] analyzed multiple cues in a unified energy minimization framework and used the model in [9] to detect salient objects. A major problem is that cluttered backgrounds usually yield higher saliencies for possessing high local contrasts. Lu et al. [11] and Liu et al. [12] regarded the saliency detection problem as binary segmentation on a graph. In [11], Lu et al. developed a hierarchical graph model and utilize concavity context to compute weights between nodes, from which the graph is bipartitioned for salient object detection. Gopalakrishnan et al. [13] and Yang et al. [14] defined the saliency as the labelling or ranking task on a graph and applied the semisupervised learning technique to infer the binary labels of the unlabeled vertices with the salient seeds. However, it is difficult to determine the number and location of salient seeds that the semisupervised method requires, which is a known problem with graph labelling. In addition, the geodesic distance metric was applied to measure the feature contrast along paths on the graph in [15].

The reason why the graph model can be associated with the saliency detection is that the prior consistency or cluster assumption [16, 17] observed in semisupervised learning or manifold learning problem, which have been demonstrated effectively to preserve the intrinsic data structure hidden in the dataset, is also appropriate for uncovering the relationships between pixels in the image. The prior consistency mainly consists of two aspects: () nearby pixels are likely to have the same saliency; () pixels on the same structure (such as an object or a homogeneous region) are likely to have the same saliency. Note that the first assumption is local, while the second one is global. The cluster assumption advises us to consider both local and global information contained in the image during learning. It is straightforward to apply cluster assumption to the graph-based saliency detection models developed in recent years, since the central idea of these methods is to find the pop-out or salient nodes while preserving the global structure hidden in the image.

Although there has been some success with the graph-based saliency detection approaches, identifying salient objects in natural scenes remains a challenge because factors such as the local or global structure information are not fully described. The graph-based semisupervised learning or manifold learning methods model the whole dataset as a graph. Similarly, the graph-based saliency detection method models the input image in the same way. In most graph-based models, the superpixels are extracted and denoted as the basic graph nodes in consideration of the computation efficiency and perception meaning. In addition, the complete graph [9, 13], nearest neighboring graph or -regular graph [13, 15], or the close-loop graph [14] is applied to simulate the local graph structure in different saliency models. However, how to estimate the weight of each edge has not been fully studied. More concretely, most of methods adopted a Gaussian function to calculate the edge weights of the graph [9, 13–15]. But the variance of the Gaussian function will affect the detection results significantly. This problem has been demonstrated in the semisupervised learning methods [18], which occurs in the graph-based saliency models (illustrated in Figure 1) as well. However, there is no reliable approach for model selection if only very few labeled seeds are available; that is, it is hard to determine optimal , as pointed out by Zhou et al. [16].