Semantic Image Retrieval Using Color Distribution and Similarity Measurement in WordNet 


Vol. 11,  No. 4, pp. 509-516, Aug.  2004
10.3745/KIPSTB.2004.11.4.509


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  Abstract

Semantic interpretation of image is incomplete without some mechanism for understanding semantic content that is not directly visible. For this reason, human assisted content-annotation through natural language is an attachment of textual description to image. However, keyword-based retrieval is in the level of syntactic pattern matching. In other words, dissimilarity computation among terms is usually done by using string matching not concept matching. In this paper, we propose a method for computerized semantic similarity calculation in WordNet space. We consider the edge, depth, link type and density as well as existence of common ancestors. Also, we have introduced method that applied similarity measurement on semantic image retrieval. To combine with the low level features, we use the spatial color distribution model. When tested on a image set of Microsoft's 'Design Gallery Live', proposed method outperforms other approach.

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[IEEE Style]

J. H. Choi, M. Y. Cho, P. K. Kim, "Semantic Image Retrieval Using Color Distribution and Similarity Measurement in WordNet," The KIPS Transactions:PartB , vol. 11, no. 4, pp. 509-516, 2004. DOI: 10.3745/KIPSTB.2004.11.4.509.

[ACM Style]

Jun Ho Choi, Mi Young Cho, and Pan Koo Kim. 2004. Semantic Image Retrieval Using Color Distribution and Similarity Measurement in WordNet. The KIPS Transactions:PartB , 11, 4, (2004), 509-516. DOI: 10.3745/KIPSTB.2004.11.4.509.

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