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A Statistical Framework for Discrete Visual Features Modeling and Classification


A Statistical Framework for Discrete Visual Features Modeling and Classification

Ghimire, Mukti Nath (2011) A Statistical Framework for Discrete Visual Features Modeling and Classification. Masters thesis, Concordia University.

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Multimedia contents are mostly described in discrete forms, so analyzing discrete data becomes an important task in many image processing and computer vision applications. One of the most used approaches for discrete data modeling is the finite mixture of multinomial distributions, considering that the events to model are independent. It, however, fails to capture the true nature in the case of sparse data and leads generally to poor biased estimates. Different smoothing techniques that reflect prior background knowledge are proposed to overcome this issue. Generalized Dirichlet distribution has suitable covariance structure, so it offers flexibility in parameter estimation; therefore, it has become a favorable choice as a prior. This specific choice, however, has its problems mainly in the estimation of the parameters, which appears to be a laborious task and can deteriorate the estimates accuracy when we consider the maximum likelihood (ML) approach.

In this thesis, we propose an unsupervised statistical approach to learn structures of this kind of data. The central ingredient in our model is the introduction of the generalized Dirichlet distribution mixture as a prior to the multinomial. An estimation algorithm for the parameters based on leave-one-out (LOO) likelihood and empirical Bayesian inference is developed. This estimation algorithm can be viewed as a hybrid expectation-maximization (EM) which alternates EM iterations with Newton-Raphson iterations using the Hessian matrix. We also propose the use of our model as a parametric basis for support vector machines (SVM) within a hybrid Generative/discriminative framework. Through a series of experiments involving scene modeling and classification using visual words and color texture modeling, we show the efficiency of the proposed approaches.

Divisions:Concordia University > Gina Cody School of Engineering and Computer Science > Electrical and Computer Engineering
Item Type:Thesis (Masters)
Authors:Ghimire, Mukti Nath
Institution:Concordia University
Degree Name:M.A. Sc.
Program:Electrical and Computer Engineering
Date:25 July 2011
Thesis Supervisor(s):Bouguila, Nizar
Keywords:Discrete features Finite mixture models Multinomial Dirichlet Generalized Dirichlet Leave-one-out likelihood SVM Generative/discriminative Scene classification Visual words
ID Code:7752
Deposited On:17 Nov 2011 18:58
Last Modified:18 Jan 2018 17:31
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