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A Lazy Processing Approach to User Relevance Feedback for Content-Based Image Retrieval

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4 Author(s)
Nilpanich, S. ; Sch. of Electr. Eng. & Comput. Sci., Univ. of Central Florida, Orlando, FL, USA ; Hua, K.A. ; Petkova, A. ; Ho, Y.H.

User Relevance feedback techniques based on learning methods such as Artificial Neural Networks and kernel machines have been widely used in content-based image retrieval. However, the traditional relevance feedback framework for existing techniques still suffers from: (1) high learning cost incurs substantial delay in responding to user relevance feedback, (2) the classifiers may be biased when the negative feedback samples out-number the positive feedback samples, and (3) The high feature dimensions compared to the size of the training set causes over fitting. We propose a new relevance feedback approach based on a lazy processing framework. This approach combines random sampling, data clustering, and ensembles of classifiers to address the aforementioned problems. Our experimental studies show that the proposed framework provides a responsive user feedback environment that is capable of outperforming the traditional approach.

Published in:

Multimedia (ISM), 2010 IEEE International Symposium on

Date of Conference:

13-15 Dec. 2010