A Fast and Accurate Video Semantic-Indexing System Using Fast MAP Adaptation and GMM Supervectors

Abstract

We propose a fast maximum a posteriori (MAP) adaptation method for video semantic indexing that uses Gaussian mixture model (GMM) supervectors. In this method, a tree-structured GMM is utilzed to decrease the computational cost, where only the output probabilities of mixture components close to an input sample are precisely calculated. 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 with that of a conventional method. The total calculation time is reduced by 56.6% while keeping the same level of the accuracy.