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Asymmetric Semi-Supervised Boosting Scheme for Interactive Image Retrieval
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  • Asymmetric Semi-Supervised Boosting Scheme for Interactive Image Retrieval
  • Asymmetric Semi-Supervised Boosting Scheme for Interactive Image Retrieval
저자명
Wu. Jun,Lu. Ming-Yu
간행물명
ETRI journal
권/호정보
2010년|32권 5호|pp.766-773 (8 pages)
발행정보
한국전자통신연구원
파일정보
정기간행물|ENG|
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이 논문은 한국과학기술정보연구원과 논문 연계를 통해 무료로 제공되는 원문입니다.
서지반출

기타언어초록

Support vector machine (SVM) active learning plays a key role in the interactive content-based image retrieval (CBIR) community. However, the regular SVM active learning is challenged by what we call "the small example problem" and "the asymmetric distribution problem." This paper attempts to integrate the merits of semi-supervised learning, ensemble learning, and active learning into the interactive CBIR. Concretely, unlabeled images are exploited to facilitate boosting by helping augment the diversity among base SVM classifiers, and then the learned ensemble model is used to identify the most informative images for active learning. In particular, a bias-weighting mechanism is developed to guide the ensemble model to pay more attention on positive images than negative images. Experiments on 5000 Corel images show that the proposed method yields better retrieval performance by an amount of 0.16 in mean average precision compared to regular SVM active learning, which is more effective than some existing improved variants of SVM active learning.