SG'11 Proceedings of the 11th international conference on Smart graphics
Browse by chunks: Topic mining and organizing on web-scale social media
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP) - Special section on ACM multimedia 2010 best paper candidates, and issue on social media
Discovering inherent event taxonomies from social media collections
Proceedings of the 2nd ACM International Conference on Multimedia Retrieval
Identification of scene locations from geotagged images
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
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As digital cameras with Global Positioning System (GPS) capability become available and people geotag their photos using other means, it is of great interest to annotate semantic events (e.g., hiking, skiing, party) characterized by a collection of geotagged photos with timestamps and GPS information at the capture. We address this emerging event classification problem by mining informative features derived from image contents and spatio-temporal traces of GPS coordinates that characterize the underlying movement patterns of various event types, both based on the entire collection as opposed to individual photos. Considering that events are better described by the co-occurrence of objects and scenes, we bundle primitive features such as color and texture histograms or GPS features to form the discriminative compositional feature. A data mining method is proposed to efficiently discover discriminative compositional features of small classification errors. A theoretical analysis is also presented to guide the selection of the data mining parameters. Upon compositional feature mining, we perform the multiclass AdaBoost to further integrate the mined compositional features. Finally, the GPS and visual modalities are united through a confidence-based fusion. Based on a dataset of more than 3000 geotagged images, experimental results have shown the synergy of all of the components in our proposed approach to event classification.