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Title: Image annotation with parametric mixture model based multi-class multi-labeling
Authors: Wang, Z
Siu, WC 
Feng, DD
Issue Date: 2008
Source: 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.
Keywords: Content-based retrieval
Image classification
Image retrieval
Publisher: IEEE
ISBN: 978-1-4244-2295-1
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.
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