Exploiting generative models in discriminative classifiers
Proceedings of the 1998 conference on Advances in neural information processing systems II
Evaluation campaigns and TRECVid
MIR '06 Proceedings of the 8th ACM international workshop on Multimedia information retrieval
A simple and efficient sampling method for estimating AP and NDCG
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
SIFT-Bag kernel for video event analysis
MM '08 Proceedings of the 16th ACM international conference on Multimedia
Image classification using super-vector coding of local image descriptors
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part V
High-Level Feature Extraction Using SIFT GMMs and Audio Models
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
q-Gaussian mixture models based on non-extensive statistics for image and video semantic indexing
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part II
q-Gaussian mixture models for image and video semantic indexing
Journal of Visual Communication and Image Representation
E-LAMP: integration of innovative ideas for multimedia event detection
Machine Vision and Applications
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We propose a fast maximum a posteriori (MAP) adaptation technique for a GMM-supervectors-based video semantic indexing system.The use of GMM supervectors is one of the state-of-the-art methods in which MAP adaptation is needed for estimating the distribution of local features extracted from video data. The proposed method cuts the calculation time of the MAP adaptation step. With the proposed method, a tree-structured GMM is constructed to quickly calculate posterior probabilities for each mixture component of a GMM. The basic idea of the tree-structured GMM is to cluster Gaussian components and approximate them with a single Gaussian. Leaf nodes of the tree correspond to the mixture components, and each non-leaf node has a single Gaussian that approximates its descendant Gaussian distributions. Experimental evaluation on the TRECVID 2010 dataset demonstrates the effectiveness of the proposed method. The calculation time of the MAP adaptation step is reduced by 76.2% compared to that of a conventional method and resulting accuracy (in terms of Mean average precision) was 10.2%.