FotoFile: a consumer multimedia organization and retrieval system
Proceedings of the SIGCHI conference on Human Factors in Computing Systems
Does organisation by similarity assist image browsing?
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Multi-view Matching for Unordered Image Sets, or "How Do I Organize My Holiday Snaps?"
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Temporal event clustering for digital photo collections
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
The MIR flickr retrieval evaluation
MIR '08 Proceedings of the 1st ACM international conference on Multimedia information retrieval
Learning similarity metrics for event identification in social media
Proceedings of the third ACM international conference on Web search and data mining
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In this paper, we propose an automatic clustering method for large photographs collections using time and content features. First, we think about various types of feature vectors that are suitable to represent time and content of photographs, and we computed the similarity measures that can be represented an affinity between these photos. Next, we consider a clustering method for photo collection. Here, we first build a coarser clustering by automatically partitioning a given photo collection into several clusters using the Mean shift clustering algorithm. Second, we construct dense clustering by optimizing a Gaussian Dirichlet process mixture model taking initial clusters model as coarser clustering result. Finally, we have conducted the experiment which is able to evaluate a performance of our clustering method for various events photos collection. Experimental results show that both three types of features and Gaussian Dirichlet process mixture model brings about higher values of accuracy and precision in the clustering of photo-collection.