Clustering techniques for personal photo album management

We propose a novel approach for the automatic representation of pictures achieving a more effective organization of personal photo albums. Images are analyzed and described in multiple representation spaces, namely, faces, background, and time of capture. 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 an RGB histogram and Gabor filter bank. Faces, time, and background information of each image in the collection is automatically organized using a 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 besides containing visually similar data. We report experimental results based on a data set of about 1000 images where automatic detection and rectification of faces lead to approximately 400 faces. Significance of clustering has been evaluated, and results are very encouraging.