Recently Inverted Beta-Liouville mixture models have emerged as an efficient paradigm for
proportional positive vectors modeling and unsupervised learning. However, little attention …

In this paper, we propose a novel hybrid discriminative learning approach based on shifted-
scaled Dirichlet mixture model (SSDMM) and Support Vector Machines (SVMs) to address …

This study presents an unsupervised novel algorithm for color image segmentation, object
detection and tracking based on unsupervised learning step followed with a post processing …

Parametric family of statistical distributions are of great importance for several applications.
In particular, we propose to investigate the generalized Gamma mixture model (MM) for …

Abstract Machine learning (ML), a branch of artificial intelligence (AI), is an area of
computational science that is concerned with the analysis and interpretation of patterns and …

Finite Gamma mixture models have proved to be flexible and can take prior information into
account to improve generalization capability, which make them interesting for several …

In this paper, we propose an efficient learning framework for both finite and infinite Gamma
mixture models. Unlike existing learning methods such as maximum-likelihood method, we …

Visual tracking is a challenging task in computer vision, which intends to estimate the motion
state of the target of interest in subsequent video frames. In that context, it is well-known that …

The goal of this paper is to develop an effective approach allowing to capture accurately the
intrinsic nature of data using an infinite shifted‐scaled Dirichlet mixture model (InSSDMM) …

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 …