Efficient graph-based search for object detection

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• 作者：Hui Wei ; ^a ; ^b ; ^{weihui@fudan.edu.cn} ; Chengzhuan Yang^a ; ^b ; ^{chengzhuanyang13@fudan.edu.cn} ; Qian Yu^a ; ^b ; ^{yuqian12@fudan.edu.cn}
• 关键词：Shape-based object detection ; Graph-based search ; Contour matching
• 刊名：Information Sciences
• 出版年：2017
• 出版时间：April 2017
• 年：2017
• 卷：385-386
• 期：Complete
• 页码：395-414
• 全文大小：5046 K
• 卷排序：385

文摘

In this paper, we focus on the object localization problem in images given a single hand-drawn example as the object model. We propose a novel framework for shape-based object detection and recognition, which we formulate as a graph-based search problem. In our method, we first propose five preprocessing procedures to reduce the irrelevant edge fragments in cluttered real images that often occur in edge detection. Then we build a graph to represent the edge images. Therefore, our goal is to find the group of adjacent nodes in the graph that match well to the model contours. Finally, we present a new evaluation method to verify the candidate hypotheses. We did experiments on the ETHZ shape classes dataset and the INRIA horses dataset. Experimental results demonstrate that the proposed method achieves not only accurate object detection but also precise contour localization in cluttered real images. Comparisons with other recent template-based matching methods further demonstrate the effectiveness and efficiency of the proposed method.