| Summary: | 碩士 === 國立中正大學 === 資訊管理學系 === 93 === The amount of visual data has been growing enormously with the expansion of WWW. From the large number of images, it is very important for users to retrieve required images via an effective mechanism. To solve these problems, Content-Based Image Retrieval was proposed.
One major limitation of content-based image retrieval is the semantic gap between low-level features of images and high-level concepts of human. This is because users usually prefer querying images by high-level concepts or semantics instead of low-level images features.
In this paper, we present an enhancement semantic-based mechanism (ESBM) to solve the problem. The mechanism is mainly based on combining the concept of relevance feedback and information filtering. Our system considers color semantics such as pretty, cheerful, etc. as one high-level conceptual query. In addition, images which have been classified into some conceptual categories are available for keyword-based queries.The system first of all learns users’ queries and cluster users into four groups by their ages, occupations, interests and gender. After training, new query results will be based on the feedbacks and user’s groups. Experimental results show that ESBM is able to enhance retrieval effectiveness compared with traditional keyword-based image retrieval systems.
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