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Title: Generalized biased discriminant analysis for content-based image retrieval
Authors: Zhang, Lining.
Wang, Lipo.
Lin, Weisi.
Keywords: DRNTU::Engineering::Electrical and electronic engineering
Issue Date: 2011
Source: Zhang, L., Wang, L., & Lin, W. (2011). Generalized Biased Discriminant Analysis for Content-Based Image Retrieval. IEEE Transactions on Systems, Man, and Cybernetics-Part B: Cybernetics, 42(1), 282-290.
Series/Report no.: IEEE transactions on systems, man, and cybernetics-Part B: cybernetics
Abstract: Biased discriminant analysis (BDA) is one of the most promising relevance feedback (RF) approaches to deal with the feedback sample imbalance problem for content-based image retrieval (CBIR). However, the singular problem of the positive within-class scatter and the Gaussian distribution assumption for positive samples are two main obstacles impeding the performance of BDA RF for CBIR. To avoid both of these intrinsic problems in BDA, in this paper, we propose a novel algorithm called generalized BDA (GBDA) for CBIR. The GBDA algorithm avoids the singular problem by adopting the differential scatter discriminant criterion (DSDC) and handles the Gaussian distribution assumption by redesigning the between-class scatter with a nearest neighbor approach. To alleviate the overfitting problem, GBDA integrates the locality preserving principle; therefore, a smooth and locally consistent transform can also be learned. Extensive experiments show that GBDA can substantially outperform the original BDA, its variations, and related support-vector-machine-based RF algorithms.
DOI: 10.1109/TSMCB.2011.2165335
Rights: © 2011 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: DOI: [].
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Journal Articles

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