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

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 -Semantics Graph, to discover the hidden semantic relationships among the semantic categories embodied in the image collection. With the -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 sema...

• Type: Conference
• Year: 2004
• Where: KDD
• Authors: Ruofei Zhang, Zhongfei (Mark) Zhang, Sandeep Khanzode
• Keywords: Data Mining, Hidden Semantic Relationships, KDD 2004, Semantic Categories, Semantic Category