Object detection via foreground contour feature selection and part-based shape model

• Published in: Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012) pp. 2524 - 2527
• Main Authors: Zhang Huigang, Wang Junxiu, Bai Xiao, Zhou Jun, Cheng Jian, Zhao Huijie
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.11.2012
• Subjects: Computational modeling; Context; Educational institutions; Feature extraction; Object detection; Shape; Training
• ISBN: 9781467322164; 1467322164
• ISSN: 1051-4651

In this paper, we propose a novel approach for object detection via foreground feature selection and part-based shape model. It automatically learns a shape model from cluttered training images without need to explicitly given bounding box on objects. Our approach commences by extracting a set of feature descriptors, and iteratively selects the foreground features using Earth Movers Distances based matching. This leads to a part-based shape model that can be used for object detection. Experimental results show that the proposed method has comparable performance with the state-of-the-art shape-based detection methods but with less requirements on the data at the training stage.