NDLI: A data mining approach to modeling relationships among categories in image collection

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A data mining approach to modeling relationships among categories in image collection

Content Provider	ACM Digital Library
Author	Zhang, Ruofei Zhang, Zhongfei Mark Khanzode, Sandeep
Abstract	This paper proposes a data mining approach to modeling relationships among categories in image collection. In our approach, with image feature grouping, a visual dictionary is created for color, texture, and shape feature attributes respectively. Labeling each training image with the keywords in the visual dictionary, a classification tree is built. Based on the statistical properties of the feature space we define a structure, called α-Semantics Graph, to discover the hidden semantic relationships among the semantic categories embodied in the image collection. With the α-Semantics Graph, each semantic category is modeled as a unique fuzzy set to explicitly address the semantic uncertainty and semantic overlap among the categories in the feature space. The model is utilized in the semantics-intensive image retrieval application. An algorithm using the classification accuracy measures is developed to combine the built classification tree with the fuzzy set modeling method to deliver semantically relevant image retrieval for a given query image. The experimental evaluations have demonstrated that the proposed approach models the semantic relationships effectively and the image retrieval prototype system utilizing the derived model is promising both in effectiveness and efficiency.
Starting Page	749
Ending Page	754
Page Count	6
File Format	PDF
ISBN	1581138881
DOI	10.1145/1014052.1014151
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2004-08-22
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Relationships Image collection Fuzzy model Semantic category
Content Type	Text
Resource Type	Article

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