This manuscript provides a comprehensive exploration of Probabilistic latent semantic
analysis (PLSA), highlighting its strengths, drawbacks, and challenges. The PLSA, originally …

In this work we developed a novel and fast traffic sign recognition system, a very important
part for advanced driver assistance system and for autonomous driving. Traffic signs play a …

Semantic image analysis is an active topic of research in computer vision and pattern
recognition. In the last decade, a huge number of related models have been proposed …

Latent topic models such as Latent Dirichlet Allocation (LDA) and probabilistic Latent
Semantic Analysis (pLSA) have demonstrated success in computer vision tasks. Most …

Topic model is a popular tool for visual concept learning. Most topic models are either
unsupervised or fully supervised. In this paper, to take advantage of both limited labeled …

Probabilistic latent semantic analysis is a statistical technique developed for information
retrieval and spanned many fields. It yields intuitive and solid results. However, the rigidity of …

Probabilistic latent semantic analysis (PLSA) is a topic model for text documents, which has
been widely used in text mining, computer vision, computational biology and so on. For …

Due to the semantic gap between visual features and semantic concepts, automatic image
annotation has become a difficult issue in computer vision recently. We propose a new …

Background: It presents a kind of multi-class image classification algorithm which is
combined with Best Versus Second Best (BVSB) active learning technology and improved …

En recherche d'information, les documents sont le plus souvent considérés comme des"
sacs-de-mots". Ce modèle ne tient pas compte de la structure temporelle du document et est …