Source Adaptation for Improved Content-Based Video Retrieval

Abstract

Adaptation of hidden Markov model (HMM) parameters to individual speakers is known to provide considerable improvements over speaker-independent speech recognition systems. This paper applies this idea of model adaptation to a content-based video retrieval system that uses HMMs, with different sources of video treated analogously to different speakers. Source-independent HMMs are adapted to each video-source using the maximum a posteriori probability (MAP) and maximum likelihood linear regression (MLLR) techniques. It is shown that MLLR is not effective in modeling source variability in video, while MAP is highly effective. An overall improvement of 39% is demonstrated in video retrieval performance on the TRECVID 2005 benchmark test over a competitive baseline system via source-adaptation and improved use of the HMM likelihoods in retrieval.