Unsupervised image classification is the process by which each image in a dataset is
identified to be a member of one of the inherent categories present in the image collection …

Recent years have witnessed a surge of interest in graph-based transductive image
classification. Existing simple graph-based transductive learning methods only model the …

How do we find all images in a larger set of images which have a specific content? Or
estimate the position of a specific object relative to the camera? Image classification …

Exploring structural and functional interactions among various brain regions enables better
understanding of pathological underpinnings of neurological disorders. Brain connectivity …

Hypergraph clustering refers to the process of extracting maximally coherent groups from a
set of objects using high-order (rather than pairwise) similarities. Traditional approaches to …

Hypergraphs were introduced in 1973 by Berg\'e. This review aims at giving some hints on
the main results that we can find in the literature, both on the mathematical side and on their …

Hyperspectral image (HSI) unmixing has attracted increasing research interests in recent
decades. The major difficulty of it lies in that the endmembers and the associated …

Graph model is emerging as a very effective tool for learning the complex structures and
relationships hidden in data. In general, the critical purpose of graph-oriented learning …

Image clustering is a critical step for the applications of content-based image retrieval, image
annotation and other high-level image processing. To achieve these tasks, it is essential to …

Hyperspectral image classification with limited number of labeled pixels is a challenging
task. In this paper, we propose a bilayer graph-based learning framework to address this …