Scenery image retrieval by meta-feature representation

Purpose - Content-based image retrieval suffers from the semantic gap problem: that images are represented by low-level visual features, which are difficult to directly match to high-level concepts in the user's mind during retrieval. To date, visual feature representation is still limited in i...

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Vydáno v:	Online information review Ročník 36; číslo 4; s. 517 - 533
Hlavní autoři:	Tsai, Chih-Fong, Lin, Wei-Chao
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Bradford Emerald Group Publishing Limited 01.01.2012 Emerald
Témata:	Algorithms Averages Categories Classification Coding Computer Graphics Decomposition Exact sciences and technology Experiments Global local relationship Image retrieval Indexing Information and communication sciences Information content Information science. Documentation Library and information science. General aspects Methods On-line systems Pattern Recognition Representation Representations Research Problems Retrieval Scenery Sciences and techniques of general use Search engines Semantics Semiotics Studies Visual Wavelet transforms Word meaning Class-specific distances Digital images Image processing Image retrieval Feature extraction Feature representation Meta-features Extraction Representation Digital image Distance
ISSN:	1468-4527, 1468-4535
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Abstract	Purpose - Content-based image retrieval suffers from the semantic gap problem: that images are represented by low-level visual features, which are difficult to directly match to high-level concepts in the user's mind during retrieval. To date, visual feature representation is still limited in its ability to represent semantic image content accurately. This paper seeks to address these issues.Design methodology approach - In this paper the authors propose a novel meta-feature feature representation method for scenery image retrieval. In particular some class-specific distances (namely meta-features) between low-level image features are measured. For example the distance between an image and its class centre, and the distances between the image and its nearest and farthest images in the same class, etc.Findings - Three experiments based on 190 concrete, 130 abstract, and 610 categories in the Corel dataset show that the meta-features extracted from both global and local visual features significantly outperform the original visual features in terms of mean average precision.Originality value - Compared with traditional local and global low-level features, the proposed meta-features have higher discriminative power for distinguishing a large number of conceptual categories for scenery image retrieval. In addition the meta-features can be directly applied to other image descriptors, such as bag-of-words and contextual features.
AbstractList	Purpose -- Content-based image retrieval suffers from the semantic gap problem: that images are represented by low-level visual features, which are difficult to directly match to high-level concepts in the user's mind during retrieval. To date, visual feature representation is still limited in its ability to represent semantic image content accurately. This paper seeks to address these issues. Design/methodology/approach -- In this paper the authors propose a novel meta-feature feature representation method for scenery image retrieval. In particular some class-specific distances (namely meta-features) between low-level image features are measured. For example the distance between an image and its class centre, and the distances between the image and its nearest and farthest images in the same class, etc. Findings -- Three experiments based on 190 concrete, 130 abstract, and 610 categories in the Corel dataset show that the meta-features extracted from both global and local visual features significantly outperform the original visual features in terms of mean average precision. Originality/value -- Compared with traditional local and global low-level features, the proposed meta-features have higher discriminative power for distinguishing a large number of conceptual categories for scenery image retrieval. In addition the meta-features can be directly applied to other image descriptors, such as bag-of-words and contextual features. Adapted from the source document. Purpose - Content-based image retrieval suffers from the semantic gap problem: that images are represented by low-level visual features, which are difficult to directly match to high-level concepts in the user's mind during retrieval. To date, visual feature representation is still limited in its ability to represent semantic image content accurately. This paper seeks to address these issues. Design/methodology/approach - In this paper the authors propose a novel meta-feature feature representation method for scenery image retrieval. In particular some class-specific distances (namely meta-features) between low-level image features are measured. For example the distance between an image and its class centre, and the distances between the image and its nearest and farthest images in the same class, etc. Findings - Three experiments based on 190 concrete, 130 abstract, and 610 categories in the Corel dataset show that the meta-features extracted from both global and local visual features significantly outperform the original visual features in terms of mean average precision. Originality/value - Compared with traditional local and global low-level features, the proposed meta-features have higher discriminative power for distinguishing a large number of conceptual categories for scenery image retrieval. In addition the meta-features can be directly applied to other image descriptors, such as bag-of-words and contextual features.
Author	Tsai, Chih-Fong Lin, Wei-Chao
Author_xml	– sequence: 1 givenname: Chih-Fong surname: Tsai fullname: Tsai, Chih-Fong organization: Department of Information Management, National Central University, Jhongli, Taiwan, ROC – sequence: 2 givenname: Wei-Chao surname: Lin fullname: Lin, Wei-Chao organization: Department of Computer Science and Information Engineering, Hwa Hsia Institute of Technology, Taipei, Taiwan, ROC
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Snippet	Purpose - Content-based image retrieval suffers from the semantic gap problem: that images are represented by low-level visual features, which are difficult to... Purpose -- Content-based image retrieval suffers from the semantic gap problem: that images are represented by low-level visual features, which are difficult...
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SubjectTerms	Algorithms Averages Categories Classification Coding Computer Graphics Decomposition Exact sciences and technology Experiments Global local relationship Image retrieval Indexing Information and communication sciences Information content Information science. Documentation Library and information science. General aspects Methods On-line systems Pattern Recognition Representation Representations Research Problems Retrieval Scenery Sciences and techniques of general use Search engines Semantics Semiotics Studies Visual Wavelet transforms Word meaning
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Title	Scenery image retrieval by meta-feature representation
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