adaptive aggregating multiresolution feature coding for image classification

Clicks: 162
ID: 155609
2014
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Abstract
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.
Reference Key
liao2014mathematicaladaptive Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Honghong Liao;Jinhai Xiang;Weiping Sun;Shengsheng Yu
Journal journal of power sources
Year 2014
DOI
10.1155/2014/847608
URL
http://dx.doi.org/10.1155/2014/847608
Keywords
civil engineering (general)mathematics engineering (general)

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