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A Model-Based Sequence Similarity with Application to Handwritten Word Spotting
Nov. 2012 (vol. 34 no. 11)
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.
Index Terms:
Hidden Markov models,Vectors,Computational modeling,Visualization,Training,Feature extraction,Handwriting recognition,hidden Markov model,Handwriting recognition,word spotting,image retrieval
Citation:
José A. Rodríguez-Serrano, F. Perronnin, "A Model-Based Sequence Similarity with Application to Handwritten Word Spotting," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 34, no. 11, pp. 2108-2120, Nov. 2012, doi:10.1109/TPAMI.2012.25
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