TY - GEN
T1 - Compositional object pattern
T2 - 19th ACM International Conference on Multimedia ACM Multimedia 2011, MM'11
AU - Tsai, Shen Fu
AU - Cao, Liangliang
AU - Tang, Feng
AU - Huang, Thomas S.
PY - 2011
Y1 - 2011
N2 - In this paper, we study the problem of recognizing events in personal photo albums. In consumer photo collections or online photo communities, photos are usually organized in albums according to their events. However, interpreting photo albums is more complicated than the traditional problem of understanding single photos, because albums generally exhibit much more varieties than single image. To solve this challenge, we propose a novel representation, called Compositional Object Pattern, which characterizes object level pattern conveying much richer semantic than low level visual feature. To interpret the rich semantics in albums, we mine frequent object patterns in the training set, and then rank them by their discriminating power. The album feature is then set as the frequencies of these frequent and discriminative patterns, called Compositional Object Pattern Frequency( COPF). We show with experimental result that our algorithm is capable of recognizing holidays with accuracy higher than the baseline method.
AB - In this paper, we study the problem of recognizing events in personal photo albums. In consumer photo collections or online photo communities, photos are usually organized in albums according to their events. However, interpreting photo albums is more complicated than the traditional problem of understanding single photos, because albums generally exhibit much more varieties than single image. To solve this challenge, we propose a novel representation, called Compositional Object Pattern, which characterizes object level pattern conveying much richer semantic than low level visual feature. To interpret the rich semantics in albums, we mine frequent object patterns in the training set, and then rank them by their discriminating power. The album feature is then set as the frequencies of these frequent and discriminative patterns, called Compositional Object Pattern Frequency( COPF). We show with experimental result that our algorithm is capable of recognizing holidays with accuracy higher than the baseline method.
KW - Algorithms
KW - Experimentation
UR - http://www.scopus.com/inward/record.url?scp=84455201946&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84455201946&partnerID=8YFLogxK
U2 - 10.1145/2072298.2072015
DO - 10.1145/2072298.2072015
M3 - Conference contribution
AN - SCOPUS:84455201946
SN - 9781450306164
T3 - MM'11 - Proceedings of the 2011 ACM Multimedia Conference and Co-Located Workshops
SP - 1361
EP - 1364
BT - MM'11 - Proceedings of the 2011 ACM Multimedia Conference and Co-Located Workshops
Y2 - 28 November 2011 through 1 December 2011
ER -