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A feature selection framework for small sampling data in content-based image retrieval system

Chung, K.P., Fung, C.C. and Wong, K.W. (2005) A feature selection framework for small sampling data in content-based image retrieval system. In: 2005 5th International Conference on Information, Communications and Signal Processing, 6-9 Dec. 2005, Bangkok, Thailand pp. 310-314.

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    Link to Published Version: http://dx.doi.org/10.1109/ICICS.2005.1689057
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    Abstract

    Content-based image retrieval (CBIR) systems have drawn interest from many researchers in recent years. Over the last few years, kernel-based approach has been a popular choice for the implementation of the relevance feedback based CBIR system. This is largely due to its ability to classify patterns with limited sample data. A long flat vector has been a popular choice for the input configuration. The reasons are because it is relatively easy to implement and more importantly, because it preserve the information of identifying the target images via different combination of image features. However, one of the biggest weaknesses of such configuration is the curse of dimensionality. This paper introduces a relevance feedback framework via the use of statistical discriminant analysis method to select only relevant feature for next image retrieval cycle. Hence, minimize the dimensionality of the feature vector. This approach has been tested with four sets of images labelled with different themes. Each set contains 500 images, 50 labelled as positive while the rest are negative. The test showed an improvement from the previous flat input vector configuration when the training samples are relatively small.

    Publication Type: Conference Paper
    Murdoch Affiliation: School of Information Technology
    Publisher: IEEE
    Copyright: (c) 2005 IEEE.
    Notes: Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
    URI: http://researchrepository.murdoch.edu.au/id/eprint/616
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