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

• Published in: PloS one Vol. 10; no. 7; p. e0131721
• Main Authors: Zhang, Yunchao, Chen, Jing, Huang, Xiujie, Wang, Yongtian
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 01.07.2015; Public Library of Science (PLoS)
• Subjects: Algorithms; Artificial Intelligence; Classification; Cluster Analysis; Computer science; Databases, Factual; Distance learning; Image classification; Image coding; Image Interpretation, Computer-Assisted - methods; Image management; Image Processing, Computer-Assisted - methods; Image retrieval; Information Storage and Retrieval; International conferences; Methods; Models, Statistical; Multimedia; Neurosciences; Pattern recognition; Pattern Recognition, Automated - methods; Probabilistic analysis; Probability; Retrieval; Software; Sparsity
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0131721

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 with 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.