Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/39905
Title: Automatic person annotation of family photo album
Authors: Zhao, M. 
Teo, Y.W.
Liu, S.
Chua, T.-S. 
Jain, R.
Issue Date: 2006
Source: Zhao, M.,Teo, Y.W.,Liu, S.,Chua, T.-S.,Jain, R. (2006). Automatic person annotation of family photo album. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 4071 LNCS : 163-172. ScholarBank@NUS Repository.
Abstract: Digital photographs are replacing tradition films in our daily life and the quantity is exploding. This stimulates the strong need for efficient management tools, in which the annotation of "who" in each photo is essential. In this paper, we propose an automated method to annotate family photos using evidence from face, body and context information. Face recognition is the first consideration. However, its performance is limited by the uncontrolled condition of family photos. In family album, the same groups of people tend to appear in similar events, in which they tend to wear the same clothes within a short time duration and in nearby places. We could make use of social context information and body information to estimate the probability of the persons' presence and identify other examples of the same recognized persons. In our approach, we first use social context information to cluster photos into events. Within each event, the body information is clustered, and then combined with face recognition results using a graphical model. Finally, the clusters with high face recognition confidence and context probabilities are identified as belonging to specific person. Experiments on a photo album containing over 1500 photos demonstrate that our approach is effective. © Springer-Verlag Berlin Heidelberg 2006.
Source Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
URI: http://scholarbank.nus.edu.sg/handle/10635/39905
ISBN: 3540360182
ISSN: 03029743
Appears in Collections:Staff Publications

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