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http://dx.doi.org/10.3745/KIPSTB.2006.13B.4.383

Multi-class Feedback Algorithm for Region-based Image Retrieval  

Ko Byoung-Chul (계명대학교 컴퓨터공학과)
Nam Jae-Yeal (계명대학교 컴퓨터공학과)
Publication Information
The KIPS Transactions:PartB / v.13B, no.4, 2006 , pp. 383-392 More about this Journal
Abstract
In this paper, we propose a new relevance feedback algorithm using Probabilistic Neural Networks(PNN) while supporting multi-class learning. Then, to validate the effectiveness of our feedback approach, we incorporate the proposed algorithm into our region-based image retrieval tool, FRIP(Finding Regions In the Pictures). In our feedback approach, there is no need to assume that feature vectors are independent, and as well as it allows the system to insert additional classes for detail classification. In addition, it does not have a long computation time for training because it only has four layers. In the PNN classification process, we store the user's entire past feedback actions as a history in order to improve performance for future iterations. By using a history, our approach can capture the user's subjective intension more precisely and prevent retrieval performance errors which originate from fluctuating or degrading in the next iteration. The efficacy of our method is validated using a set of 3000 images derived from a Corel-photo CD.
Keywords
Region-Based Image Retrieval; Feedback Learning; Frip; PNN; Feedback History;
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