Abstract
This paper addresses the problem of data vectors modeling, classification and recognition using infinite mixture models, which have been shown to be an effective alternative to finite mixtures in terms of selecting the optimal number of clusters. In this work, we propose a novel approach for localized features modelling using an infinite mixture model based on multivariate generalized Normal distributions (inMGNM). The statistical mixture is learned via a nonparametric MCMC-based Bayesian approach in order to avoid the crucial problem of model over-fitting and to allow uncertainty in the number of mixture components. Robust descriptors are derived from encoding features with the Fisher vector method, which considers higher order statistics. These descriptors are combined with a linear support vector machine classifier in order to achieve higher accuracy. The efficiency and merits of the proposed nonparametric Bayesian learning approach, while comparing it to other different methods, are demonstrated via two challenging applications, namely texture classification and human activity categorization.
Highlights
Introduction and Related WorksIn recent years, there has been major progress in Statistical Machine Learning (SML)with unsupervised learning problems such as clustering, classification and recognition for both univariate and multivariate data
The rest of this paper is organized as follows: In Section 3, we present our proposed infinite mixture model as well as a fully Bayesian learning approach; in Section 4, we study the performance of the developed framework on the basis of different applications and data sets; in Section 5, we conclude the current work
We describe the different steps of the proposed framework, such as the feature extraction step, the modelling step with the infinite mixture model and learning the process via priors and posteriors
Summary
Introduction and Related WorksIn recent years, there has been major progress in Statistical Machine Learning (SML)with unsupervised learning problems such as clustering, classification and recognition for both univariate and multivariate data. There has been an increasing interest in the use of both supervised and unsupervised learning methods to assign or group similar objects into homogeneous and disjoint clusters. Such methods are used for constructing classifiers to effectively recognize objects on the basis of discriminative visual features. Mixture models are widely used in scientific problems, where multidimensional objects are to be clustered or classified. In this situation, the data should be modelled in terms of a mixture of many components. Finite Gaussian mixture models were employed successfully in many applications [7,8]
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