A training algorithm for optimal margin classifiers
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
Text classification using string kernels
The Journal of Machine Learning Research
Video Google: A Text Retrieval Approach to Object Matching in Videos
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
A Performance Evaluation of Local Descriptors
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Comparison of Affine Region Detectors
International Journal of Computer Vision
Edit distance-based kernel functions for structural pattern classification
Pattern Recognition
International Journal of Computer Vision
Evaluating bag-of-visual-words representations in scene classification
Proceedings of the international workshop on Workshop on multimedia information retrieval
Unsupervised Learning of Human Action Categories Using Spatial-Temporal Words
International Journal of Computer Vision
Video Event Recognition Using Kernel Methods with Multilevel Temporal Alignment
IEEE Transactions on Pattern Analysis and Machine Intelligence
Video event detection using motion relativity and visual relatedness
MM '08 Proceedings of the 16th ACM international conference on Multimedia
Event detection in field sports video using audio-visual features and a support vector Machine
IEEE Transactions on Circuits and Systems for Video Technology
Localization and recognition of the scoreboard in sports video based on SIFT point matching
MMM'11 Proceedings of the 17th international conference on Advances in multimedia modeling - Volume Part II
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The recognition of events in videos is a relevant and challenging task of automatic semantic video analysis. At present one of the most successful frameworks, used for object recognition tasks, is the bag-of-words (BoW) approach. However this approach does not model the temporal information of the video stream. In this paper we present a method to introduce temporal information within the BoW approach. Events are modeled as a sequence composed of histograms of visual features, computed from each frame using the traditional BoW model. The sequences are treated as strings where each histogram is considered as a character. Event classification of these sequences of variable size, depending on the length of the video clip, are performed using SVM classifiers with a string kernel that uses the Needlemann-Wunsch edit distance. Experimental results, performed on two datasets, soccer video and TRECVID 2005, demonstrate the validity of the proposed approach.