CLOSET+: searching for the best strategies for mining frequent closed itemsets
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
From frequent itemsets to semantically meaningful visual patterns
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Direct mining of discriminative and essential frequent patterns via model-based search tree
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Annotating photo collections by label propagation according to multiple similarity cues
MM '08 Proceedings of the 16th ACM international conference on Multimedia
MM '09 Proceedings of the 17th ACM international conference on Multimedia
PCA-SIFT: a more distinctive representation for local image descriptors
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Album-based object-centric event recognition
ICME '11 Proceedings of the 2011 IEEE International Conference on Multimedia and Expo
Discovering inherent event taxonomies from social media collections
Proceedings of the 2nd ACM International Conference on Multimedia Retrieval
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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.