An automatic image annotation method based on the mutual K-nearest neighbor graph

Yu-tang Guo, Bin Luo

Abstract: In order to improve the accuracy of the image annotation, an automatic image annotation method based on mutual K-nearest neighbor graph (MKNN) is proposed. The proposed algorithm describes the relationship between low-level features, annotation words and image by a mutual K-nearest neighbor graph. Semantic information is extracted by exploiting the mutual relationship of two nodes in the mutual K-nearest neighbor graph. Inverse document frequency (IDF) is introduced to adjust the weights of edges between the image node and its annotation word's node, which overcomes the deviation caused by high-frequency words. Experimental results in Corel image dataset show that the proposed algorithm improves effectively the image annotation performance..