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User intention modeling for interactive image retrieval

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3 Author(s)
Jingyu Cui ; Stanford Univ. CA, Stanford, CA, USA ; Fang Wen ; Xiaoou Tang

We propose three innovative interactive methods to let computer better understand user intention in content-based image retrieval: 1. Smart intention list induces user intention, thereby improves search results by intention-specific search schema; 2. Reference strokes interaction allows user to specify in detail about the intention by pointing out interested regions; 3. Natural user feedback easily collects data of user relevance feedbacks to boost the performance of the system. Systematic user study shows that the proposed interactive mechanism improves search efficiency, reduces user workload, and enhances user experience.

Published in:

Multimedia and Expo (ICME), 2010 IEEE International Conference on

Date of Conference:

19-23 July 2010