Two-stage localization for image labeling

Authors:
Yanyun Qu;Diwei Wu;Yanyun Cheng;Cheng Chen
Affiliations:
Computer Science Department, Xiamen University, P.R. China;Computer Science Department, Xiamen University, P.R. China;Computer Science Department, Xiamen University, P.R. China;Computer Science Department, Xiamen University, P.R. China
Venue:
PCM'10 Proceedings of the 11th Pacific Rim conference on Advances in multimedia information processing: Part I
Year:
2010

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Abstract

The well-built dataset is a pre-requisite for object categorization. However, the processes of collecting and labeling the images are laborious and monotonous. In this paper, we focus on an automatic labeling of images by using a bounding box for each visual object. We propose a two-stage localization approach for image labeling which combines the Efficient Subwindow Search scheme with Multiple Instance Learning. We firstly detect the object coarsely by the the Efficient Subwindow Search scheme, and then we finely localize the object by Multiple Instance learning. Our approach has two advantages, one is to speed up the object search, and the other is to locate the object precisely in a tighter box than the Efficient Subwindow Search scheme. We evaluate the image labeling performance by the detection precision and the detection consistency with the ground truth label. Our approach is simple and fast in object localization. The experiment results demonstrate that our approach is more effective and accurate than the BOW model in the precision and consistency of detection.