Automatic event-based indexing of multimedia content using a joint content-event model
Proceedings of the 2nd ACM international workshop on Events in multimedia
Event detection and recognition for semantic annotation of video
Multimedia Tools and Applications
A feature sequence kernel for video concept classification
MMM'11 Proceedings of the 17th international conference on Advances in multimedia modeling - Volume Part I
Sequence-based kernels for online concept detection in video
AIEMPro '11 Proceedings of the 2011 ACM international workshop on Automated media analysis and production for novel TV services
Sequence kernels for clustering and visualizing near duplicate video segments
MMM'12 Proceedings of the 18th international conference on Advances in Multimedia Modeling
Reordering video shots for event classification using bag-of-words models and string kernels
Proceedings of the 27th Conference on Image and Vision Computing New Zealand
Searching informative concept banks for video event detection
Proceedings of the 3rd ACM conference on International conference on multimedia retrieval
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Event recognition is a crucial task to provide high-level semantic description of the video content. The bag-of-words (BoW) approach has proven to be successful for the categorization of objects and scenes in images, but it is unable to model temporal information between consecutive frames. In this paper we present a method to introduce temporal information for video event recognition within the BoW approach. Events are modeled as a sequence composed of histograms of visual features, computed from each frame using the traditional BoW. The sequences are treated as strings (phrases) where each histogram is considered as a character. Event classification of these sequences of variable length, depending on the duration of the video clips, are performed using SVM classifiers with a string kernel that uses the Needlemann-Wunsch edit distance. Experimental results, performed on two domains, soccer videos and a subset of TRECVID 2005 news videos, demonstrate the validity of the proposed approach.