文章基本信息

标题：Asymmetric Semi-Supervised Boosting Scheme for Interactive Image Retrieval
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作者：Wu, Jun ; Lu, Ming-Yu
期刊名称：ETRI Journal
印刷版ISSN：1225-6463
电子版ISSN：2233-7326
出版年度：2010
卷号：32
期号：5
页码：766-773
DOI：10.4218/etrij.10.1510.0016
语种：English
出版社：Electronics and Telecommunications Research Institute
摘要：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.
关键词：Interactive image retrieval;support vector machines;semi-supervised learning;active learning;boosting