International Journal of Computer Vision
Fast multiresolution image querying
SIGGRAPH '95 Proceedings of the 22nd annual conference on Computer graphics and interactive techniques
Content-Based Image Retrieval at the End of the Early Years
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image Indexing Using Color Correlograms
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Temporal Color Correlograms for Video Retrieval
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 1 - Volume 1
Video retrieval using spatio-temporal descriptors
MULTIMEDIA '03 Proceedings of the eleventh ACM international conference on Multimedia
Large-Scale Concept Ontology for Multimedia
IEEE MultiMedia
Detecting semantic concepts from video using temporal gradients and audio classification
CIVR'03 Proceedings of the 2nd international conference on Image and video retrieval
On clustering and retrieval of video shots through temporal slices analysis
IEEE Transactions on Multimedia
Motion analysis and segmentation through spatio-temporal slices processing
IEEE Transactions on Image Processing
Multimedia search and retrieval: new concepts, system implementation, and application
IEEE Transactions on Circuits and Systems for Video Technology
Shot-boundary detection: unraveled and resolved?
IEEE Transactions on Circuits and Systems for Video Technology
Combined key-frame extraction and object-based video segmentation
IEEE Transactions on Circuits and Systems for Video Technology
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Detection of events and actions in video entails substantial processing of very large, even open-ended, video streams. The video information retrieval community has substantially focused on a single frame approach for retrieval and classification tasks. While this approach is sufficiently powerful for certain types of semantic concepts, there are more complicated categories such as events or motion that require more than that provided by a single frame. We present a simple and effective way of extracting time variant information in video data using three perspective views into a video stack.