In this paper we present a novel approach for personal photo album management. Pictures are analyzed and described in three representation spaces, namely, faces, background and time of capture. Faces are automatically detected and rectified using a probabilistic feature extraction technique. Face representation is then produced by computing PCA (Principal Component Analysis). 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. Each image in the collection is then automatically organized using a mean-shift clustering technique. While many systems manage faces and typically allow queries about them we use a common approach to manage multiple aspects, that is, queries regarding people, time and background are dealt with in a homogenous way. We report experimental results on a realistic set, i.e., a personal photo album, of about 2000 images where automatic detection and rectification of faces lead to approximately 800 faces. Significance of clustering has been evaluated and results are very interesting.
|Numero di pagine||15|
|Stato di pubblicazione||Published - 2010|
All Science Journal Classification (ASJC) codes