To learn a well-performed image annotation model, a large number of labeled samples are
usually required. Although the unlabeled samples are readily available and abundant, it is a …

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

By virtue of complexing Ni/Co/Mn ions with glucose under solvothermal conditions, well-
defined spherical Ni/Co/Mn-gluconate with tunable size and elemental composition is …

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 …

Refining image annotation has become one of the core research topics in computer vision
and pattern recognition due to its great potentials in image retrieval. However, it is still in its …

Semantic gap has become a bottleneck of content-based image retrieval in recent years. In
order to bridge the gap and improve the retrieval performance, automatic image annotation …

A new approach is presented to improve the classification performance of medical X‐ray
images based on the combination of generative and discriminative classification approach …

The demand for automatically classification of medical X-ray images is rising faster than
ever. In this paper, an approach is presented to gain high accuracy rate for those classes of …

In this paper, we present a novel image annotation method that leverages on the
advantages of both generative and discriminative models. To label an image, we first identify …

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 …