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

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 fe...

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Bibliographic Details
Published inProceedings 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
LanguageEnglish
Published IEEE 01.11.2012
Subjects
Computational modeling
Context
Educational institutions
Feature extraction
Object detection
Shape
Training
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Summary: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.
ISBN:9781467322164
1467322164
ISSN:1051-4651
2831-7475