Image Retrieval Based on Similarity Learning

Most traditional image retrieval approaches are based on text surrounding images and fail to capture the content information of the images. These approaches are able to satisfy the present demands. To overcome the limitations of previous approaches, in this paper, we propose a new image retrieval method based on probability similarity learning which is able to exploit probabilistic distribution information. This method involves three stages, image feature extraction, similarity learning and image retrieval. To evaluate the proposed approach, Corel image dataset is used. Corel image dataset contain nine categories which are buildings, birds, flowers, people, trees, elephants, white clouds, mountains and automotive. The experimental results show that the probability similarity learning based approach achieves high accurate in image retrieval network images. For the proposed retrieval method, the accuracy is greatly dependent on the weights. Thus, determining the weights is the next emphasis in the future research.