International Journal of Computer Vision
The nature of statistical learning theory
The nature of statistical learning theory
SIMPLIcity: Semantics-Sensitive Integrated Matching for Picture LIbraries
IEEE Transactions on Pattern Analysis and Machine Intelligence
Invariant Features for Gray Scale Images
Mustererkennung 1995, 17. DAGM-Symposium
SIMBA - Search IMages By Appearance
Proceedings of the 23rd DAGM-Symposium on Pattern Recognition
A short introduction to learning with kernels
Advanced lectures on machine learning
Generalizing discriminant analysis using the generalized singular value decomposition
IEEE Transactions on Pattern Analysis and Machine Intelligence
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It is known that no single descriptor is powerful enough to encompass all aspects of image content, i.e. each feature extraction method has its own view of the image content. A possible approach to cope with that fact is to get a whole view of the image(object). Then using machine learning approach from user’s Relevance feedback to obtain a reduced feature. In this paper, we concentrate on some points about Biased Discriminant Analysis / Kernel Biased Discriminant Analysis (BDA/KBDA) based machine learning approach for CBIR. The contributions of this paper are: 1. using generalized singular value decomposition (GSVD) based approach solve the small sample size problem in BDA/KBDA and 2. using histogram intersection as a kernel for KBDA. Experiments show that this kind of kernel gets improvement compare to other common kernels.