Image retrieval: Ideas, influences, and trends of the new age
ACM Computing Surveys (CSUR)
Introduction to Information Retrieval
Introduction to Information Retrieval
On the sampling of web images for learning visual concept classifiers
Proceedings of the ACM International Conference on Image and Video Retrieval
Finding media illustrating events
Proceedings of the 1st ACM International Conference on Multimedia Retrieval
Identifying content for planned events across social media sites
Proceedings of the fifth ACM international conference on Web search and data mining
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In recent years, the emergence of social media on the Internet has derived many of interesting research and applications. In this paper, a novel framework is proposed to model the visual appearance of social events using automatically collected training samples on the basis of photo context analysis. While collecting positive samples can be achieved easily thanks to explicitly identifying tags, finding representative negative samples from the vast amount of irrelevant multimedia documents is a more challenging task. Here, we argue and demonstrate that the most common negative sample, originating from the same location as the event to be modeled, are best suited for the task. A novel ranking approach is devised to select a set of negative samples. The visual event models are learned from automatically collected samples using SVM. The results reported here show that the event models are effective to filter out irrelevant photos and perform with a high accuracy on various social events categories.