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所属单位：自动化学院
发表刊物：J. Electron. Imaging
摘要：Region proposal algorithms are beneficial for enhancing the performance of object detection and recognition methods. We present a method for grouping region proposals based on perceptual grouping principles. The grouping principles are simulated to extract image features, and the region proposals are segmented by solving a sequence of parametric maxflow problems. In order to extract complex objects from natural images, the element connectedness cue is introduced in the parametric energy functions. This newly introduced cue is propitious to group objects with diversified patterns. To effectively fuse the grouping principles, a multiclassify-based learning algorithm is proposed to optimize an ensemble of binary segmentation models. The training samples are first divided into groups to pretrain each individual model, and the algorithm adaptively adjusts the sample groups in the iteration procedure to learn an optimal set of models. We conduct the experiments on the PASCAL Visual Object Classes Challenge 2012 segmentation dataset but also in the context of region proposals in optical remote sensing images, and the results show that the proposed method can achieve a favorable performance compared to the existing algorithms. © 2019 SPIE and IS&T.
ISSN号：1017-9909
是否译文：否
发表时间：2019-09-01
合写作者：Wang, Zhengbing,徐贵力,Han, Xiaodong,王正盛
通讯作者：程月华