Automatic Image Representation and Clustering on Mobile Devices

In this paper a novel approach for the automatic representation of pictures on mobile devices is proposed. With the wide diffusion of mobile digital image acquisition devices, the need of managing a large number of digital images is quickly increasing. In fact the storage capacity of such devices allow users to store hundreds or even thousands, of pictures that, without a proper organization, become useless. Users may be interested in using (i.e., browsing, saving, printing and so on) a subset of stored data according to some particular picture properties. A content-based description of each picture is needed to perform on-board image indexing. In our work the images are analyzed and described in three representation spaces, namely, faces, background and time of capture. Faces are automatically detected and a face representation is produced by 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 bank. Temporal data is obtained through the extraction of EXIF (Exchangeable Image File Format) data. Faces, background and time information of each image in the collection is automatically organized using a mean-shift clustering technique. Significance of clustering has been evaluated on a realistic set of about 1000 images and results are very interesting.