The nature of statistical learning theory
The nature of statistical learning theory
Making large-scale support vector machine learning practical
Advances in kernel methods
Probabilistic feature relevance learining for content-based image retrieval
Computer Vision and Image Understanding - Special issue on content-based access for image and video libraries
Support vector machine active learning for image retrieval
MULTIMEDIA '01 Proceedings of the ninth ACM international conference on Multimedia
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
On the efficient evaluation of probabilistic similarity functions for image retrieval
IEEE Transactions on Information Theory
Relevance feedback: a power tool for interactive content-based image retrieval
IEEE Transactions on Circuits and Systems for Video Technology
Quantization-based probabilistic feature modeling for kernel design in content-based image retrieval
MIR '06 Proceedings of the 8th ACM international workshop on Multimedia information retrieval
Proceedings of the international workshop on Workshop on multimedia information retrieval
An efficient and effective image representation for region-based image retrieval
Proceedings of the 2nd International Conference on Interaction Sciences: Information Technology, Culture and Human
An efficient region-based image representation using Legendre color distribution moments
Proceedings of the 2nd International Conference on Interaction Sciences: Information Technology, Culture and Human
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Relevance feedback is a critical component for content-based retrieval systems. Effective learning algorithms are needed to accurately and quickly capture the user's query concept, under the daunting challenges of high dimensional data and small number of training samples. It has been shown that support vector machines (SVMs) can be used to conduct effective relevance feedback in content-based image retrieval. Most recent work along these lines has focused on how to customize SVM classification for the particular problem of interest. However, not much attention has been to paid to the design of novel kernel functions specifically tailored for relevance feedback problems and traditional kernels have been directly used in these applications. In this paper, we propose an approach to derive an information divergence based kernel given the user's preference. Our proposed kernel function naturally takes into account the statistics of the data that is available during relevance feedback for the purpose of discriminating between relevant and non-relevant images. Experiments show that the new kernel achieves significantly higher (about $17%) retrieval accuracy than the standard radial basis function (RBF) kernel, and can thus become a valid alternative to traditional kernels for SVM-based active learning in relevance feedback applications