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Instance based personalized multi-form image browsing and retrieval
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AI
DOI：10.1007/s11042-012-1249-z.png)
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摘要
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It is important to adapt and personalize image browsing and retrieval systems based on users' preferences for improved user experience and satisfaction. In this paper, we present a novel instance based personalized multi-form image representation with implicit relevance feedback and adaptive weighting approach for image browsing and retrieval systems. In the proposed system, images are grouped into forms, which represent different information on images such as location, content etc. We conducted user interviews on image browsing, sharing and retrieval systems for understanding image browsing and searching behaviors of users. Based on the insights gained from the user interview study we propose an adaptive weighting method and implicit relevance feedback for multi-form structures that aim to improve the efficiency and accuracy of the system. Statistics of the past actions are considered for modeling the target of the users. Thus, on each iteration weights of the forms are updated adaptively. Moreover, retrieval results are modified according to the users' preferences on iterations in order to improve personalized user experience. The proposed method has been evaluated and results are illustrated in the paper. It is shown that, satisfactory improvements can be achieved with proposed approaches in the multi-form scheme.
Keyword:
Content-based image indexing and retrieval
Image browsing
Implicit feedback
Personalized and adaptive image image browsing
期刊
IF:
3
论文数:
1.9W
被引数:
3.2W
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