Image Clustering via Combined Visual and Annotation Information
Cui Jun-ju · Harbin Ligong Daxue xuebao · 2014
To effectively employ annotation information and visual information for image clustering,a novel algorithm based on the co-occurrence of the visual and annotation words is proposed. In the vision feature space,Kmeans algorithm is utilized to cluster the feature into visual words,namely the cluster centers. In the annotation word space,a visual-annotation co-occurrence matrix is constructed by computing the statistical distribution of the annotation words under each corresponding visual cluster. Then,the annotation feature with its corresponding visual information embedded can be extracted. Finally the LDA( latent dirichlet allocation) topic model is used to cluster the images. The numerical experiments on Pascal VOC 2007 database show that the proposed method can effectively take advantage of the complementary visual and annotation information to improve the performance of clustering algorithms.