Time as essence for photo browsing through personal digital libraries
Proceedings of the 2nd ACM/IEEE-CS joint conference on Digital libraries
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
Automatic Semantic Annotation of Real-World Web Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Event recognition from photo collections via PageRank
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Event classification in personal image collections
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
Evaluating Color Descriptors for Object and Scene Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Real-Time Visual Concept Classification
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
Discovering inherent event taxonomies from social media collections
Proceedings of the 2nd ACM International Conference on Multimedia Retrieval
Categorization of a collection of pictures into structured events
Proceedings of the 2nd ACM International Conference on Multimedia Retrieval
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The aim of this paper is threefold: (a) to introduce a dataset for the recognition of events and sub-events in photographs taken by common users; (b) to propose event-based classification to achieve a more accurate labeling of event-related photo collections; (c) to use time clustering information to improve the sub-event recognition in an efficient Bag of Features classification approach. The dataset is organized according to event models and provides a collection of sample instances that allow the comparison of different recognition systems. On this basis, we will demonstrate how the use of time clustering together with multiple image visual features can outperform single image classification.