ETRI Journal

  • 제32권5호
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  • Pages.766-773
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  • 2010
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  • 1225-6463(pISSN)
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  • 2233-7326(eISSN)
한국전자통신연구원 (Electronics and Telecommunications Research Institute)
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Asymmetric Semi-Supervised Boosting Scheme for Interactive Image Retrieval

  • Wu, Jun (School of Information Science & Technology, Dalian Maritime University) ;
  • Lu, Ming-Yu (School of Information Science & Technology, Dalian Maritime University)
  • 투고 : 2010.02.09
  • 심사 : 2010.08.04
  • 발행 : 2010.10.31
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초록

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.

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참고문헌

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피인용 문헌

  1. 센서스 정보 및 전력 부하를 활용한 전력 수요 예측 vol.18, pp.3, 2013, https://doi.org/10.9723/jksiis.2013.18.3.035