A fast MAP adaptation technique for gmm-supervector-based video semantic indexing systems

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%.