Compositional object pattern: A new model for album event recognition

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.