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.