Dissertation > Industrial Technology > Automation technology,computer technology > Computing technology,computer technology > Computer applications > Information processing (information processing) > Pattern Recognition and devices > Image recognition device

The Research and Implemention of Image Retrieval Based on User Interested Feature

Author LiuXiaoZhen
Tutor ZhangTianWen
School Harbin Institute of Technology
Course Computer Science and Technology
Keywords content-based image retrieval Web server log analysis user interested feature-based image retrieval
CLC TP391.41
Type Master's thesis
Year 2008
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The combination of Web and content-based image retrieval(CBIR) is gradually becoming the future study and development trend of content-based image retrieval system。In Web-oriented system, we get users interested feature by tracking the users’accessing records and retrieve with sample image. user interested feature-based image retrieval has effectively improved the efficiency of the users’search.In this paper, we completed the following work:(1) Accessing of user interested images. By web server logs analysis and information filtering, users delete useless web logs and web log domains , get the visit records and access to users interested image, and establish a visit index table of interest image , with users’intent.(2) Color and texture feature-based image retrieval. In this paper, we extract 20-dimensional color feature with HSV space based on the cumulative color histogram and 16-dimensional texture feature with Gray co-occurrence matrix, measure similarity between two pictures respectively with histogram intersection and continental distance,normalize the results and Synthesis the total similarity.(3) User interested feature-based image retrieval. It contains the following methods: Single-user interested feature based image retrieval and Multi-user interested feature based image retrieval.Single-user interested feature based image retrieval combine sample image feature and single-user interested feature, which is obtained by compositing user interested image and forgotten factor.Multi-user interested feature based image retrieval use sample and user interested feature, which is the result of multi-user interested feature clustering.In conclusion, the two methods could improve the efficiency of retrieval through experiments.

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