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Issue No. 11 - Nov. (2012 vol. 34)
ISSN: 0162-8828
pp: 2108-2120
José A. Rodríguez-Serrano , Textual & Visual Pattern Anal. Group, Xerox Res. Centre Eur., Meylan, France
F. Perronnin , Textual & Visual Pattern Anal. Group, Xerox Res. Centre Eur., Meylan, France
This paper proposes a novel similarity measure between vector sequences. We work in the framework of model-based approaches, where each sequence is first mapped to a Hidden Markov Model (HMM) and then a measure of similarity is computed between the HMMs. We propose to model sequences with semicontinuous HMMs (SC-HMMs). This is a particular type of HMM whose emission probabilities in each state are mixtures of shared Gaussians. This crucial 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 similarity between two SC-HMMs can be simplified to a Dynamic Time Warping (DTW) between their mixture weight vectors, which significantly reduces the computational cost. Experiments are carried out on a handwritten word retrieval task in three different datasets-an in-house dataset of real handwritten letters, the George Washington dataset, and the IFN/ENIT dataset of Arabic handwritten words. These experiments show that the proposed similarity outperforms the traditional DTW between the original sequences, and the model-based approach which uses ordinary continuous HMMs. We also show that this increase in accuracy can be traded against a significant reduction of the computational cost.
Hidden Markov models, Vectors, Computational modeling, Visualization, Training, Feature extraction, Handwriting recognition, hidden Markov model, Handwriting recognition, word spotting, image retrieval

F. Perronnin and J. A. Rodríguez-Serrano, "A Model-Based Sequence Similarity with Application to Handwritten Word Spotting," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 34, no. , pp. 2108-2120, 2012.
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