title: A Probabilistic Analysis of Sparse Coded Feature Pooling and Its Application for Image Retrieval.
abstract: 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.
authors.affiliation: School of computer science & technology, Beijing Institute of Technology, Beijing, China.
authors.forename: Yunchao
authors.initials: Y
authors.lastname: Zhang
authors.affiliation: School of optoelectronics, Beijing Institute of Technology, Beijing, China.
authors.forename: Jing
authors.initials: J
authors.lastname: Chen
authors.affiliation: School of optoelectronics, Beijing Institute of Technology, Beijing, China.
authors.forename: Xiujie
authors.initials: X
authors.lastname: Huang
authors.affiliation: School of optoelectronics, Beijing Institute of Technology, Beijing, China; School of computer science & technology, Beijing Institute of Technology, Beijing, China.
authors.forename: Yongtian
authors.initials: Y
authors.lastname: Wang
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D016000:Cluster Analysis; D016208:Databases, Factual; D007090:Image Interpretation, Computer-Assisted / Q000379:methods; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D016247:Information Storage and Retrieval; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods*; D011336:Probability; D012984:Software