Image retrieval from the web using multiple features

Purpose - The main obstacle in realising semantic-based image retrieval from the web is that it is difficult to capture semantic description of an image in low-level features. Text-based keywords can be generated from web documents to capture semantic information for narrowing down the search space....

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Vydáno v:	Online information review Ročník 33; číslo 6; s. 1169 - 1188
Hlavní autoři:	Vadivel, A., Sural, Shamik, Majumdar, A.K.
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Bradford Emerald Group Publishing Limited 01.01.2009 Emerald
Témata:	Active Learning Algorithms Exact sciences and technology Feature selection Histograms Image retrieval Imagery Indexing Information and communication sciences Information Retrieval Information science. Documentation Integrative approach Internet Keywords Learning Strategies Library and information science. General aspects Online information retrieval Pornography Queries Retrieval Sciences and techniques of general use Scientific Concepts Search engines Semantics Semiotics Statistical analysis Use and user studies. Information needs Visual Perception World Wide Web Worldwide web Search engines Worldwide web Internet Image retrieval Search engine World wide web Use study
ISSN:	1468-4527, 1468-4535
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Abstract	Purpose - The main obstacle in realising semantic-based image retrieval from the web is that it is difficult to capture semantic description of an image in low-level features. Text-based keywords can be generated from web documents to capture semantic information for narrowing down the search space. The combination of keywords and various low-level features effectively increases the retrieval precision. The purpose of this paper is to propose a dynamic approach for integrating keywords and low-level features to take advantage of their complementary strengths.Design methodology approach - Image semantics are described using both low-level features and keywords. The keywords are constructed from the text located in the vicinity of images embedded in HTML documents. Various low-level features such as colour histograms, texture and composite colour-texture features are extracted for supplementing keywords.Findings - The retrieval performance is better than that of various recently proposed techniques. The experimental results show that the integrated approach has better retrieval performance than both the text-based and the content-based techniques.Research limitations implications - The features of images used for capturing the semantics may not always describe the content.Practical implications - The indexing mechanism for dynamically growing features is challenging while practically implementing the system.Originality value - A survey of image retrieval systems for searching images available on the internet found that no internet search engine can handle both low-level features and keywords as queries for retrieving images from WWW so this is the first of its kind.
AbstractList	Purpose The main obstacle in realising semanticbased image retrieval from the web is that it is difficult to capture semantic description of an image in lowlevel features. Textbased keywords can be generated from web documents to capture semantic information for narrowing down the search space. The combination of keywords and various lowlevel features effectively increases the retrieval precision. The purpose of this paper is to propose a dynamic approach for integrating keywords and lowlevel features to take advantage of their complementary strengths. Designmethodologyapproach Image semantics are described using both lowlevel features and keywords. The keywords are constructed from the text located in the vicinity of images embedded in HTML documents. Various lowlevel features such as colour histograms, texture and composite colourtexture features are extracted for supplementing keywords. Findings The retrieval performance is better than that of various recently proposed techniques. The experimental results show that the integrated approach has better retrieval performance than both the textbased and the contentbased techniques. Research limitationsimplications The features of images used for capturing the semantics may not always describe the content. Practical implications The indexing mechanism for dynamically growing features is challenging while practically implementing the system. Originalityvalue A survey of image retrieval systems for searching images available on the internet found that no internet search engine can handle both lowlevel features and keywords as queries for retrieving images from WWW so this is the first of its kind. Purpose - The main obstacle in realising semantic-based image retrieval from the web is that it is difficult to capture semantic description of an image in low-level features. Text-based keywords can be generated from web documents to capture semantic information for narrowing down the search space. The combination of keywords and various low-level features effectively increases the retrieval precision. The purpose of this paper is to propose a dynamic approach for integrating keywords and low-level features to take advantage of their complementary strengths.Design methodology approach - Image semantics are described using both low-level features and keywords. The keywords are constructed from the text located in the vicinity of images embedded in HTML documents. Various low-level features such as colour histograms, texture and composite colour-texture features are extracted for supplementing keywords.Findings - The retrieval performance is better than that of various recently proposed techniques. The experimental results show that the integrated approach has better retrieval performance than both the text-based and the content-based techniques.Research limitations implications - The features of images used for capturing the semantics may not always describe the content.Practical implications - The indexing mechanism for dynamically growing features is challenging while practically implementing the system.Originality value - A survey of image retrieval systems for searching images available on the internet found that no internet search engine can handle both low-level features and keywords as queries for retrieving images from WWW so this is the first of its kind. PurposeThe main obstacle in realising semantic-based image retrieval from the web is that it is difficult to capture semantic description of an image in low-level features. Text-based keywords can be generated from web documents to capture semantic information for narrowing down the search space. The combination of keywords and various low-level features effectively increases the retrieval precision. The purpose of this paper is to propose a dynamic approach for integrating keywords and low-level features to take advantage of their complementary strengths. Design/methodology/approachImage semantics are described using both low-level features and keywords. The keywords are constructed from the text located in the vicinity of images embedded in HTML documents. Various low-level features such as colour histograms, texture and composite colour-texture features are extracted for supplementing keywords. FindingsThe retrieval performance is better than that of various recently proposed techniques. The experimental results show that the integrated approach has better retrieval performance than both the text-based and the content-based techniques. Research limitations/implicationsThe features of images used for capturing the semantics may not always describe the content. Practical implicationsThe indexing mechanism for dynamically growing features is challenging while practically implementing the system. Originality/valueA survey of image retrieval systems for searching images available on the internet found that no internet search engine can handle both low-level features and keywords as queries for retrieving images from WWW so this is the first of its kind. Adapted from the source document. Purpose - The main obstacle in realising semantic-based image retrieval from the web is that it is difficult to capture semantic description of an image in low-level features. Text-based keywords can be generated from web documents to capture semantic information for narrowing down the search space. The combination of keywords and various low-level features effectively increases the retrieval precision. The purpose of this paper is to propose a dynamic approach for integrating keywords and low-level features to take advantage of their complementary strengths. Design/methodology/approach - Image semantics are described using both low-level features and keywords. The keywords are constructed from the text located in the vicinity of images embedded in HTML documents. Various low-level features such as colour histograms, texture and composite colour-texture features are extracted for supplementing keywords. Findings - The retrieval performance is better than that of various recently proposed techniques. The experimental results show that the integrated approach has better retrieval performance than both the text-based and the content-based techniques. Research limitations/implications - The features of images used for capturing the semantics may not always describe the content. Practical implications - The indexing mechanism for dynamically growing features is challenging while practically implementing the system. Originality/value - A survey of image retrieval systems for searching images available on the internet found that no internet search engine can handle both low-level features and keywords as queries for retrieving images from WWW so this is the first of its kind. [PUBLICATION ABSTRACT]
Author	Sural, Shamik Majumdar, A.K. Vadivel, A.
Author_xml	– sequence: 1 givenname: A. surname: Vadivel fullname: Vadivel, A. organization: Department of Computer Applications, National Institute of Technology, Tiruchirappalli, India – sequence: 2 givenname: Shamik surname: Sural fullname: Sural, Shamik organization: School of Information Technology, Indian Institute of Technology, Kharagpur, India – sequence: 3 givenname: A.K. surname: Majumdar fullname: Majumdar, A.K. organization: Department of Computer Science and Engineering, Indian Institute of Technology, Kharagpur, India
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Snippet	Purpose - The main obstacle in realising semantic-based image retrieval from the web is that it is difficult to capture semantic description of an image in... Purpose The main obstacle in realising semanticbased image retrieval from the web is that it is difficult to capture semantic description of an image in... PurposeThe main obstacle in realising semantic-based image retrieval from the web is that it is difficult to capture semantic description of an image in...
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SubjectTerms	Active Learning Algorithms Exact sciences and technology Feature selection Histograms Image retrieval Imagery Indexing Information and communication sciences Information Retrieval Information science. Documentation Integrative approach Internet Keywords Learning Strategies Library and information science. General aspects Online information retrieval Pornography Queries Retrieval Sciences and techniques of general use Scientific Concepts Search engines Semantics Semiotics Statistical analysis Use and user studies. Information needs Visual Perception World Wide Web Worldwide web
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