A Probabilistic Analysis of Sparse Coded Feature Pooling and Its Application for Image Retrieval

Yunchao Zhang, Jing Chen, Xiujie Huang, Yongtian Wang, Rongrong Ji
<span title="2015-07-01">2015</span> <i title="Public Library of Science (PLoS)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/s3gm7274mfe6fcs7e3jterqlri" style="color: black;">PLoS ONE</a> </i> &nbsp;
Feature coding and pooling as a key component of image retrieval have been widely studied over the past several years. Recently sparse coding with max-pooling is regarded as the state-of-the-art for image classification. However there is no comprehensive study concerning the application of sparse coding for image retrieval. In this paper, we first analyze the effects of different sampling strategies for image retrieval, then we discuss feature pooling strategies on image retrieval performance
more &raquo; ... th a probabilistic explanation in the context of sparse coding framework, and propose a modified sum pooling procedure which can improve the retrieval accuracy significantly. Further we apply sparse coding method to aggregate multiple types of features for large-scale image retrieval. Extensive experiments on commonly-used evaluation datasets demonstrate that our final compact image representation improves the retrieval accuracy significantly.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1371/journal.pone.0131721">doi:10.1371/journal.pone.0131721</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/26132080">pmid:26132080</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC4489107/">pmcid:PMC4489107</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/7gezpztqsrbhfb5wjfk6wfad7m">fatcat:7gezpztqsrbhfb5wjfk6wfad7m</a> </span>
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