9 April 2007 Fusion and kernel type selection in adaptive image retrieval
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Abstract
In this work we investigate the relationships between features representing images, fusion schemes for these features and kernel types used in an Web-based Adaptive Image Retrieval System. Using the Kernel Rocchio learning method, several kernels having polynomial and Gaussian forms are applied to general images represented by annotations and by color histograms in RGB and HSV color spaces. We propose different fusion schemes, which incorporate kernel selector component(s). We perform experiments to study the relationships between a concatenated vector and several kernel types. Experimental results show that an appropriate kernel could significantly improve the performance of the retrieval system.
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Anca Doloc-Mihu, Anca Doloc-Mihu, Vijay V. Raghavan, Vijay V. Raghavan, } "Fusion and kernel type selection in adaptive image retrieval", Proc. SPIE 6571, Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2007, 657107 (9 April 2007); doi: 10.1117/12.720117; https://doi.org/10.1117/12.720117
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