Web image gathering with a part-based object recognition method

We propose a new Web image gathering system which employs a part-based object recognition method. The novelty of our work is introducing the bag-of-keypoints representation into an Web image gathering task instead of color histogram or segmented regions our previous system used. The bag-of-keypoints representation has been proven that it has the excellent ability to represent image concepts in the context of visual object categorization / recognition in spite of its simplicity. Most of object recognition work assumed that complete training data is available. On the other hand, in the Web image gathering task, since images associated with the given keywords are gathered from the Web fully-automatically, complete training images cannot be available. In this paper, we combine the HTML-based automatic positive training image selection and the bag-of-keypoints-based image selection with an SVM which is a supervised machine learning method. This combination enables the system to gather many images related to given concepts with high precision fully automatically needing no human intervention. Our main objective is to examine if the bag-of-keypoints model is also effective for the Web image gathering task where training images always include some noise. By the experiments, we show the new system outperforms our previous systems, other systems and Google Image Search greatly.