CSDL Home IEEE Transactions on Pattern Analysis & Machine Intelligence 2012 vol.34 Issue No.11 - Nov.
Issue No.11 - Nov. (2012 vol.34)
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
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TPAMI.2012.25
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
José A. Rodríguez-Serrano, F. Perronnin, "A Model-Based Sequence Similarity with Application to Handwritten Word Spotting", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol.34, no. 11, pp. 2108-2120, Nov. 2012, doi:10.1109/TPAMI.2012.25