WEB IMAGE GATHERING WITH REGION-BASED BAG-OF-FEATURES AND MULTIPLE INSTANCE LEARNING

We propose a new Web image gathering system which employs the region-based bag-of-features representation and multiple instance learning. The contribution of this work is introducing the region-based bag-of-features representation into an Web image gathering task where training data is incomplete and having proved its effectiveness by comparing the proposed method with the normal whole-image-based bag-of-features representation. In our method, first, we perform region segmentation for an image, and next we generate a bag-of-features vector for each region. One image is represented by a set of bag-of-features vectors in this paper, while one image is represented by just one bag-of-features vector in the normal bag-of-features representation which is very popular for visual object categorization tasks recently. Several works on Web image selection with bag-of-features have been proposed so far. However, in case that the training data includes much noise, sufficient results could not be obtained. In this paper, we divide images into regions and classify each region with multiple-instance support vector machine (mi-SVM) instead of classifying whole images. By this region-based classification, we can separate foreground regions from background regions and achieve more effective image training from incomplete training data. By the experiments, we show that the results by the proposed methods outperformed the results by the whole-image-based bag-of-visual-words and the normal support vector machine.