关键词: 距离测度学习, 近邻传播（AP）聚类算法, 图像标注

Abstract: It annotates the image for effective use of image low-level visual feature and semantic feature and puts forward a kind of improved affinity propagation cluster annotation model. Firstly, it uses the semi-supervised distance metric learning algorithm to acquire new distance measure after training combined with image semantic information. Then it uses new distance measure to cluster image with affinity propagation for every kind of images, generates cluster center of various kinds of images, calculates the average distance from will-be annotated images to cluster center of various kinds of images and determines the category of will-be annotated images. Finally, it calculates the distance from will-be annotated images to various kinds of intra-class cluster center, determines the intra-class category of will-be annotated images and summarizes the marked words of this kind image as marked words of will-be annotated images. After experiments on databases of Corel5K and NUS-WIDE, it is verified that this method is successful to improve the accuracy of image annotation.

Key words: distance metric learning, Affinity Propagation（AP）, image annotation