Ranking Saliency

Kernelized Subspace Ranking for Saliency Detection

Mesh saliency via ranking unsalient patches in a descriptor space

This paper presents a novel mesh saliency detection approach based on manifold ranking in a descriptor space. Starting from the over-segmented patches of a mesh, we compute a descriptor vector for each patch based on Zernike coefficients, and the local distinctness of each patch by a center-surround operator. Patches with small or high local distinctness are named as background or foreground pa...

Ranking Text Units According to Textual Saliency, Connectivity and Topic Aptness

An efficient use of lexical cohesion is described for ranking text units according to their contribution in defining the meaning of a text (textual saliency), their ability to form a cohesive sub-text (textual connectivity) and the extent and effectiveness to which they address the different topics which characterize the subject matter of the text (topic aptness). A specific application is also...

Visual Saliency Weighting and Cross-Domain Manifold Ranking for Sketch-Based Image Retrieval

A Sketch-Based Image Retrieval (SBIR) algorithm compares a linedrawing sketch with images. The comparison is made difficult by image background clutter. A query sketch includes an object of interest only, while database images would also contain background clutters. In addition, variability of hand-drawn sketches, due to “stroke noise” such as disconnected and/or wobbly lines, also makes the co...

Saliency Sandbox - Bottom-up Saliency Framework

Saliency maps are used to predict the visual stimulus raised from a certain region in a scene. Most approaches to calculate the saliency in a scene can be divided into three consecutive steps: extraction of feature maps, calculation of activation maps, and the combination of activation maps. In the past two decades, several new saliency estimation approaches have emerged. However, most of these...