关键词: 高斯核, 混合核, SIFT, 最大最小聚类, 协作式标注, 垃圾标签

Abstract: The accuracy of the collaborative tagging image retrieval is lower because of the inaccuracy of user’s annotation. Existing spam tag detection methods tend to focus on label itself, ignoring the correlation between collaborative label and image. Analyzing the correlation of collaborative tagging image visual content and image tags, the spam tag detection method of collaboration annotation based on visual content of collaborative tagging image is proposed. The method analyze visual content of images which have the same tag and design different kernel functions for color and SIFT feature subset. The two features will be mapped form low dimensional space to high dimensional character space, while the mixedkernel function is established. Finally, the images which have the same tag is clustered by maxmin distance means, and the tag of images in the class which has a few images are spam tags because of weak correlation. The experimental results show that the method can improve the accuracy of the tag spam detection on collaborative annotation images.

Key words: Gaussian kernel, mixed-kernel, SIFT, max-min cluster, collaborative annotation, spam tag