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Simultaneous Object Detection and Segmentation by Boosting Local Shape Feature based Classifier

This paper proposes an approach to simultaneously detect and segment objects of a known category. Edgelet features are used to capture the local shape of the objects. For each feature a pair of base classifiers for detection and segmentation is built. The base segmentor is designed to predict the per-pixel figure-ground assignment around a neighborhood of the edgelet based on the feature response. The neighborhood is represented as an effective field which is determined by the shape of the edgelet. A boosting algorithm is used to learn the ensemble classifier with cascade decision strategy from the base classifier pool. The simultaneousness is achieved for both training and testing. The system is evaluated on a number of public image sets and compared with several previous methods.