Image indexing method based on clustering via Info-Kmeans under pair constraints - Wen-jie LIU; Zhi-ang WU; Jie CAO; Jin-gui PAN (Tongxin xuebao)
Constructing high-quality content-based image indexing is fairly difficult due to the large amount of noise in the data set and the high-dimension and the sparseness of the image data.To meet this challenge,a novel noise-filtering and clustering was proposed using Info-Kmeans based image indexing construction method. Firstly,a noise-filtering me-thod using the cosine interesting patterns was presented. Secondly,a novel Info-Kmeans algorithm was proposed which could avoid the zero-feature dilemma caused by the use of KL-divergence and exploit the prior knowledge in the form of pair constraints. The experimental results on the two image data sets,LFW and Oxford_5K,well demonstrate that: noise filter can improve the clustering performance remarkably and the novel Info-Kmeans algorithm yields better results than the existing clustering tool.