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A survey of content-based image retrieval with high-level semantics (2007)
| Citations: | 150 - 5 self |
BibTeX
@MISC{Liu07asurvey,
author = {Ying Liu and Dengsheng Zhang and Guojun Lu and Wei-ying Ma},
title = { A survey of content-based image retrieval with high-level semantics},
year = {2007}
}
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Abstract
In order to improve the retrieval accuracy of content-based image retrieval systems, research focus has been shifted from designing sophisticated low-level feature extraction algorithms to reducing the ‘semantic gap ’ between the visual features and the richness of human semantics. This paper attempts to provide a comprehensive survey of the recent technical achievements in high-level semantic-based image retrieval. Major recent publications are included in this survey covering different aspects of the research in this area, including low-level image feature extraction, similarity measurement, and deriving high-level semantic features. We identify five major categories of the state-of-the-art techniques in narrowing down the ‘semantic gap’: (1) using object ontology to define high-level concepts; (2) using machine learning methods to associate low-level features with query concepts; (3) using relevance feedback to learn users’ intention; (4) generating semantic template to support high-level image retrieval; (5) fusing the evidences from HTML text and the visual content of images for WWW image retrieval. In addition, some other related issues such as image test bed and retrieval performance evaluation are also discussed. Finally, based on existing technology and the demand from real-world applications, a few promising future research directions are suggested.
Keyphrases
content-based image retrieval high-level semantics semantic gap high-level image retrieval similarity measurement content-based image retrieval system relevance feedback object ontology state-of-the-art technique visual feature high-level semantic feature high-level concept related issue major recent publication different aspect user intention recent technical achievement low-level feature high-level semantic-based image retrieval www image retrieval promising future research direction low-level image feature extraction visual content image test bed human semantics retrieval accuracy query concept major category real-world application html text comprehensive survey semantic template retrieval performance evaluation research focus sophisticated low-level feature extraction algorithm
