计算机科学
公制(单位)
人工智能
机器学习
分类器(UML)
代表(政治)
模式识别(心理学)
关系(数据库)
数据挖掘
政治学
运营管理
政治
经济
法学
作者
Jingjing Tang,Yingjie Tian,Yingjie Tian
标识
DOI:10.1016/j.eswa.2021.116117
摘要
Image classification is a critical and meaningful task in image retrieval, recognition and object detection. In this paper, three-side efforts are taken to accomplish this task. First, visual features with multi-instance representation are extracted to characterize the image due to the merits of bag-of-words representations. And a new distance function is designed for bags, which measures the relationship between bags more precisely. Second, the idea of multi-view learning is implemented since multiple views encourage the classifier to be more consistent and accurate. Last but not least, the metric learning technique is explored by optimizing the joint conditional probability to pursue view-dependent metrics and the importance weights of the newly-designed distance in multi-view scenario. Therefore, we propose a multi-view multi-instance metric learning method named MVMIML for image classification, which integrates the advantages of the multi-view multi-instance representation and metric learning into a unified framework. To solve MVMIML, we adopt the alternate iteration optimization algorithm and analyze the corresponding computational complexity. Numerical experiments verify the advantages of the new distance function and the effectiveness of MVMIML. • MVMIML integrates multi-view multi-instance features into metric learning framework. • A new distance function is designed for bags to measures the relation between bags. • Alternating iteration optimization algorithm is adopted to solve MVMIML. • Comprehensive experiments verify the effectiveness of our proposed models.
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