Abstract
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.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.