The LIVE-project: retrieval experiments based on evaluation viewpoints
SIGIR '85 Proceedings of the 8th annual international ACM SIGIR conference on Research and development in information retrieval
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Modeling score distributions for combining the outputs of search engines
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
Support vector machines for histogram-based image classification
IEEE Transactions on Neural Networks
Score distribution approach to automatic kernel selection for image retrieval systems
ISMIS'06 Proceedings of the 16th international conference on Foundations of Intelligent Systems
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The goal of this paper is to investigate the selection of the kernel for a Web-based AIRS. Using the Kernel Rocchio learning method, several kernels having polynomial and Gaussian forms are applied to general images represented by color histograms in RGB and HSV color spaces. Experimental results on these collections show that performance varies significantly between different kernel types and that choosing an appropriate kernel is important. Then, based on these results, we propose a method for selecting the kernel type that uses the score distribution models. Experimental results on our data show that the proposed method is effective for our system.