ACM Computing Surveys (CSUR)
Content-Based Image Retrieval at the End of the Early Years
IEEE Transactions on Pattern Analysis and Machine Intelligence
How do people manage their digital photographs?
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Temporal event clustering for digital photo collections
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
Toward a Common Event Model for Multimedia Applications
IEEE MultiMedia
Event composition operators: ECO
EiMM '09 Proceedings of the 1st ACM international workshop on Events in multimedia
Translating Journalists' Requirements into Features for Image Search
VSMM '09 Proceedings of the 2009 15th International Conference on Virtual Systems and Multimedia
DENCLUE 2.0: fast clustering based on kernel density estimation
IDA'07 Proceedings of the 7th international conference on Intelligent data analysis
Evaluating Color Descriptors for Object and Scene Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Content without context is meaningless
Proceedings of the international conference on Multimedia
Semantic label sharing for learning with many categories
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part I
Automated event clustering and quality screening of consumer pictures for digital albuming
IEEE Transactions on Multimedia
Modeling and representing events in multimedia
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Indexing media by personal events
Proceedings of the 2nd ACM International Conference on Multimedia Retrieval
Machine analysis and recognition of social contexts
Proceedings of the 14th ACM international conference on Multimodal interaction
Proceedings of the 20th ACM international conference on Multimedia
Proceedings of the 3rd ACM conference on International conference on multimedia retrieval
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We detect and arrange events in private photo archives by putting these photos into context. The problem is seen as a fully automated mining in one's personal life and behavior without actually recognizing the content of the photos. To this end, we build a contextual meaningful hierarchy of events. With the analysis of very simple cues of time, space and perceptual visual appearance we are refining and validating the event borders and their relation in an iterative way. Beginning with discriminating between routine and unusual events, we are able to robustly recognize the basic nature of an event. Further combination of the given cues efficiently gives a hierarchy of events that coincides with the given ground-truth at an F-measure of 0.83 for event detection and 0.70 for its hierarchical representation. We process the given task in a fully unsupervised and computationally inexpensive manner. Using standard clustering and machine learning techniques, sparse events in the collection would tend to be neglected by automated approaches. Opposed to these methods, the proposed approach is invariant to the distribution of the photo collection regarding the sparsity and denseness in time, space and visual appearance. This is improved by introducing a momentum of attraction measure for a meaningful representation of personal events.