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A hierarchical nonparametric discriminant analysis approach for a content-based image retrieval system

Chung, K.P. and Fung, C.C.ORCID: 0000-0001-5182-3558 (2005) A hierarchical nonparametric discriminant analysis approach for a content-based image retrieval system. In: ICEBE 2005: IEEE International Conference on e-Business Engineering, 12-18 Oct. 2005, Beijing pp. 346-350.

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This paper proposes a Hierarchical Nonparmetric Discriminant Analysis (HNDA) content-based image retrieval (CBIR) system for E-Business applications. It has the potential to become an important and integral component for future e-Business applications. Developments in CBIR have drawn interest from many researchers and practitioners in recent years. The challenge is how to retrieve the most appropriate or relevant images at the fastest speed. To increase the retrieval speed, most of the systems pre-process the stored images and extract out the essential features. Such scheme only works well for the server type database system. Such approach is not feasible for systems that analyze images in real-time. In this paper, a hierarchical multi-layer statistical discriminant framework is proposed. The system is able to select the most appropriate features by analyzing the newly received images, and then apply a Relevance Feedback (RF) approach to improve the retrieval accuracy. As the number of features being analyzed is less, an improvement in performance is achieved.

Item Type: Conference Paper
Murdoch Affiliation(s): School of Information Technology
Publisher: IEEE
Copyright: © 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.
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