A lazy processing approach to user relevance feedback for content-based image retrieval

Sirikunya Nilpanich, Kien A. Hua, Antoniya Petkova, Yao H. Ho

研究成果: 書貢獻/報告類型會議貢獻

7 引文 斯高帕斯(Scopus)

摘要

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 local 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.

原文英語
主出版物標題Proceedings - 2010 IEEE International Symposium on Multimedia, ISM 2010
頁面342-346
頁數5
DOIs
出版狀態已發佈 - 2010 十二月 1
事件2010 IEEE International Symposium on Multimedia, ISM 2010 - Taichung, 臺灣
持續時間: 2010 十二月 132010 十二月 15

出版系列

名字Proceedings - 2010 IEEE International Symposium on Multimedia, ISM 2010

其他

其他2010 IEEE International Symposium on Multimedia, ISM 2010
國家臺灣
城市Taichung
期間10/12/1310/12/15

ASJC Scopus subject areas

  • Computer Graphics and Computer-Aided Design
  • Human-Computer Interaction

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