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This paper points out that different local feature points provide different impacts to near-duplicate detection and related applications. Aiming to automatic representative photo selection, we develop three feature classification methods, i.e., point-based, region-based, and pLSA-based classification, to differentiate local feature points described by SIFT descriptors. We investigate the performance of these classification methods, and discuss how they influence near-duplicate detection and extended applications. Experiments show that, with effective feature classification, more accurate representative selection results can be achieved.

For consumer photos, this work clusters faces with large variations in lighting, pose, and expression. After matching face images by local feature points, we transform matching situations into a novel representation called visual sentences. Then, visual language models are constructed to describe the dependency of image patches on faces. With the probabilistic framework, we develop a clustering algorithm to group the same individual's face images into the same cluster. An interesting observation about evaluating face clustering performance is proposed, and we demonstrate the superiority of the proposed visual language model approach.