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Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/1908

Title: Image annotation with parametric mixture model based multi-class multi-labeling
Authors: Wang, Zhiyong
Siu, Wan-chi
Feng, D. David
Subjects: Content-based retrieval
Image classification
Image retrieval
Issue Date: 2008
Publisher: IEEE
Citation: Proceedings of the 2008 IEEE 10th Workshop on Multimedia Signal Processing : 8-10 October, 2008, Cairns, Australia, p. 634-639.
Abstract: Image annotation, which labels an image with a set of semantic terms so as to bridge the semantic gap between low level features and high level semantics in visual information retrieval, is generally posed as a classification problem. Recently, multi-label classification has been investigated for image annotation since an image presents rich contents and can be associated with multiple concepts (i.e. labels). In this paper, a parametric mixture model based multi-class multi-labeling approach is proposed to tackle image annotation. Instead of building classifiers to learn individual labels exclusively, we model images with parametric mixture models so that the mixture characteristics of labels can be simultaneously exploited in both training and annotation processes. Our proposed method has been benchmarked with several state-of-the-art methods and achieved promising results.
Description: DOI: 10.1109/MMSP.2008.4665153
Rights: © 2008 IEEE. 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.
This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.
Type: Conference Paper
URI: http://hdl.handle.net/10397/1908
ISBN: 978-1-4244-2295-1
Appears in Collections:EIE Conference Papers & Presentations

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