Title
Unsupervised clustering in personal photo collections
Abstract
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.
Year
DOI
Venue
2008
10.1007/978-3-642-14758-6_12
Adaptive Multimedia Retrieval
Keywords
Field
DocType
representation space,automatic detection,rgb histogram,gabor filter energy,effective representation,mean-shift clustering technique,background information,personal photo collection,unsupervised clustering,personal photo library,automatic organization,personal photo album,mean shift
Histogram,Computer vision,Face space,Pattern recognition,Computer science,Gabor filter,RGB color model,Artificial intelligence,Probabilistic logic,Cluster analysis
Conference
Volume
ISSN
ISBN
5811
0302-9743
3-642-14757-7
Citations 
PageRank 
References 
0
0.34
21
Authors
3
Name
Order
Citations
PageRank
Edoardo Ardizzone123940.79
Marco La Cascia265571.39
Filippo Vella313825.37