@inproceedings{Rodriguez:09cvpr,
	abstract = {This article proposes a novel similarity measure between vector sequences. Recently, a model-based approach was introduced to address this issue. It consists in modeling each sequence with a continuous Hidden Markov Model (CHMM) and computing a probabilistic measure of similarity between C-HMMs. In this paper we propose to model sequences with semi-continuous HMMs (SC-HMMs): the Gaussians of the SC-HMMs are constrained to belong to
a shared pool of Gaussians. This constraint provides two major benefits. First, the a priori information contained in the common set of Gaussians leads to a more accurate estimate of the HMM parameters. Second, the computation of a probabilistic similarity between two SC-HMMs
can be simplified to a Dynamic Time Warping (DTW) between their mixture weight vectors, which reduces significantly the computational cost. Experimental results on a handwritten word retrieval task show that the proposed similarity outperforms the traditional DTW between the original sequences, and the model-based approach which uses C-HMMs. We also show that this increase in accuracy can be traded against a significant reduction of the computational cost (up to 100 times).},
	author = {Rodr\'{i}guez-Serrano, Jos\'{e} A. and Perronnin, Florent and S\'{a}nchez, Gemma and Llad\'{o}s, Josep},
	booktitle = {Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition (CVPR'09)},
	title = {A similarity measure between vector sequences with application to handwritten word image retrieval},
	year = {2009}
}