Abstract
This paper presents a novel multi-dimensional hidden Markov model approach to tackle the complex issue of image modeling. We propose a set of efficient algorithms that avoids the exponential complexity of regular multi-dimensional HMMs for the most frequent algorithms (Baum-Welch and Viterbi) due to the use of a random dependency tree (DT-HMM). We provide the theoretical basis for these algorithms, and we show that their complexity remains as small as in the uni-dimensional case. A number of possible applications are given to illustrate the genericity of the approach. Experimental results are also presented in order to demonstrate the potential of the proposed DT-HMM for common image analysis tasks such as object segmentation, and tracking.
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Merialdo, B., Jiten, J., Galmar, E., Huet, B. (2007). A New Approach to Probabilistic Image Modeling with Multidimensional Hidden Markov Models. In: Marchand-Maillet, S., Bruno, E., Nürnberger, A., Detyniecki, M. (eds) Adaptive Multimedia Retrieval: User, Context, and Feedback. AMR 2006. Lecture Notes in Computer Science, vol 4398. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71545-0_8
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DOI: https://doi.org/10.1007/978-3-540-71545-0_8
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