21 July 2017 Multi-instance learning based on instance consistency for image retrieval
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Proceedings Volume 10420, Ninth International Conference on Digital Image Processing (ICDIP 2017); 104204J (2017) https://doi.org/10.1117/12.2281540
Event: Ninth International Conference on Digital Image Processing (ICDIP 2017), 2017, Hong Kong, China
Abstract
Multiple-instance learning (MIL) has been successfully utilized in image retrieval. Existing approaches cannot select positive instances correctly from positive bags which may result in a low accuracy. In this paper, we propose a new image retrieval approach called multiple instance learning based on instance-consistency (MILIC) to mitigate such issue. First, we select potential positive instances effectively in each positive bag by ranking instance-consistency (IC) values of instances. Then, we design a feature representation scheme, which can represent the relationship among bags and instances, based on potential positive instances to convert a bag into a single instance. Finally, we can use a standard single-instance learning strategy, such as the support vector machine, for performing object-based image retrieval. Experimental results on two challenging data sets show the effectiveness of our proposal in terms of accuracy and run time.
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Miao Zhang, Miao Zhang, Zhize Wu, Zhize Wu, Shouhong Wan, Shouhong Wan, Lihua Yue, Lihua Yue, Bangjie Yin, Bangjie Yin, } "Multi-instance learning based on instance consistency for image retrieval", Proc. SPIE 10420, Ninth International Conference on Digital Image Processing (ICDIP 2017), 104204J (21 July 2017); doi: 10.1117/12.2281540; https://doi.org/10.1117/12.2281540
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