How context helps: A discriminative codeword selection method for object detection

We first propose in this paper to localize objects in images based on the models learned from the weakly labeled images. This task is termed as region of interest (ROI) detection. Local features such as SIFT or HOG are extracted and the discriminative words from clustered codewords based on SIFT and HOG are selected to model the objects. Then how to find the discriminative words to model the object is important. Existing ROI detection methods consider the information from the foreground objects by selecting the words appearing more in the images belonging to one specific image class. Considering the information from background/context is also helpful for object detection and classification, we propose to select the discriminative words which appear more in the foreground/object and less in the background/context. Second, another task is to give the class label (object in this setting) for a given image and also give the position of the object appearing in the image. This task is termed as objection detection. A normal way for this task after ROI is to extract features from the detected regions and not from the whole image. Since the discriminative words extracted during ROI detection has good discriminative ability, we propose to use these words for object detection. Experimental results on PASCAL VOC 2006 dataset and a larger dataset containing 29 classes demonstrate the effectiveness of the proposed method.