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Mathematical Problems in Engineering
Volume 2014 (2014), Article ID 847608, 10 pages
Research Article

Adaptive Aggregating Multiresolution Feature Coding for Image Classification

1School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China
2College of Informatics, Huazhong Agricultural University, Wuhan 430070, China

Received 12 July 2014; Accepted 3 September 2014; Published 6 November 2014

Academic Editor: Mohamed Djemai

Copyright © 2014 Honghong Liao 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.


The Bag of Visual Words (BoW) model is one of the most popular and effective image classification frameworks in the recent literature. The optimal formation of a visual vocabulary remains unclear, and the size of the vocabulary also affects the performance of image classification. Empirically, larger vocabulary leads to higher classification accuracy. However, larger vocabulary needs more memory and intensive computational resources. In this paper, we propose a multiresolution feature coding (MFC) framework via aggregating feature codings obtained from a set of small visual vocabularies with different sizes, where each vocabulary is obtained by a clustering algorithm, and different clustering algorithm discovers different aspect of image features. In MFC, feature codings from different visual vocabularies are aggregated adaptively by a modified Online Passive-Aggressive Algorithm under the histogram intersection kernel, which lead to a closed-form solution. Experiments demonstrate that the proposed method (1) obtains the same if not higher classification accuracy than the BoW model with a large visual vocabulary; and (2) needs much less memory and computational resources.