Abstract
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.
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Mei, L., Brunner, G., Setia, L., Burkhardt, H. (2005). Kernel Biased Discriminant Analysis Using Histogram Intersection Kernel for Content-Based Image Retrieval. In: Gallagher, M., Hogan, J.P., Maire, F. (eds) Intelligent Data Engineering and Automated Learning - IDEAL 2005. IDEAL 2005. Lecture Notes in Computer Science, vol 3578. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11508069_9
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DOI: https://doi.org/10.1007/11508069_9
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