Unsupervised Clustering in Personal Photo Collections

In this paper we propose a probabilistic approach for the automatic organization of collected pictures aiming at more effective representation in personal photo albums. Images are analyzed and described in two representation spaces, namely, faces and background. Faces are automatically detected, rectified and represented projecting the face itself in a common low dimensional eigenspace. Backgrounds are represented with low-level visual features based on RGB histogram and Gabor filter energy. Face and background information of each image in the collection is automatically organized by mean-shift clustering technique. Given the particular domain of personal photo libraries, where most of the pictures contain faces of a relatively small number of different individuals, clusters tend to be semantically significant beyond containing visually similar data. We report experimental results based on a dataset of about 1000 images where automatic detection and rectification of faces lead to approximately 300 faces. Significance of clustering has been evaluated and results are very encouraging.