Generalized Biased Discriminant Analysis for Content-Based Image Retrieval

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...

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Bibliographic Details
Published in:IEEE transactions on cybernetics 2012-02, Vol.42 (1), p.282-290
Main Authors: Zhang, Lining, Wang, Lipo, Lin, Weisi
Format: Article
Language:English
Subjects:
Algorithms
Artificial Intelligence
Biased discriminant analysis (BDA)
content-based image retrieval (CBIR)
Cybernetics
Data Mining - methods
differential scatter discriminant criterion (DSDC)
Discriminant Analysis
Feedback
Image Interpretation, Computer-Assisted - methods
Image retrieval
Kernel
Manifolds
Negative feedback
Normal distribution
Pattern Recognition, Automated - methods
Radio frequencies
Radio frequency
Radiology Information Systems
relevance feedback (RF)
Scatter
Studies
Subtraction Technique
Symmetric matrices
Transforms
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Summary: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.
ISSN:1083-4419
2168-2267
1941-0492
2168-2275
DOI:10.1109/TSMCB.2011.2165335