Content quality based image retrieval with multiple instance boost ranking

Most previous works treated image retrieval as a classification problem or a similarity measurement problem. In this paper, we propose a new idea for image retrieval, in which we regard image retrieval as a ranking issue by evaluating image content quality. Based on the content preference between the images, the image pairs are organized to build the data set for rank learning. Because image content generally is disclosed by image patches with meaningful objects, each image is looked as one bag, and the regions inside are the corresponding instances. In order to save the computation cost, the instances in the image are the rectangle regions and the integral histogram is applied to speed up histogram feature extraction. Due to the feature dimension is high, we propose a boost-based multiple instance learning for image retrieval. Based on different assumptions in multiple instance setting, Mean, Max and TopK ranking models are developed with Boost learning. Experiments on the real-world images from Flickr, Pisca, and Google shows that the power of the proposed method.