Efficient scene image clustering for internet collections

This paper proposes an efficient approach to find clusters of spatially related scene images collected from the website. Our method firstly builds a guide table, in which the ranked results are given according to the relevance scores of image pairs obtained by the image retrieval methods. Then the image clusters are generated by repeatedly choosing a seed image and performing query expansion directed by the guide table. In the query process, feature matching is performed by using an affine invariant constraint which is presented to effectively reject outliers of the image feature correspondences. The proposed image clustering approach has been tested on the Bell Tower dataset consisting of more than 1K images which are collected from the photo-sharing website Flickr.com. The experimental results demonstrate the efficiency and effectiveness of our method.