title: A model-based sequence similarity with application to handwritten word spotting.
abstract: 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.
authors.affiliation: Textual and Visual Pattern Analysis Group, Xerox Research Centre Europe, Meylan, France. Jose-Antonio.Rodriguez@xrce.xerox.com
authors.forename: José A
authors.initials: JA
authors.lastname: Rodríguez-Serrano
authors.forename: Florent
authors.initials: F
authors.lastname: Perronnin
mesh_terms: D000465:Algorithms; D001699:Biometry / Q000379:methods; D003198:Computer Simulation; D001330:Electronic Data Processing / Q000379:methods*; D006236:Handwriting; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D016247:Information Storage and Retrieval / Q000379:methods*; D008390:Markov Chains; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods*