This paper describes an efficient approach for the detection of level-line junctions in images. Potential junctions are exhibited independent from noise by their consistent local level-variation. Then, level-lines are tracked through junctions in descending the level-line flow. Flow junctions are extracted as image primitives to support matching in many applications. The primitive is robust aga...

The primate visual system continuously selects spatial proscribed regions, features or objects for further processing. These selection mechanisms--collectively termed selective visual attention--are guided by intrinsic, bottom-up and by task-dependent, top-down signals. While much psychophysical research has shown that overt and covert attention is partially allocated based on saliency-driven e...

With the growing interest in computational models of visual attention, saliency prediction has become an important research topic in computer vision. Over the past years, many different successful saliency models have been proposed especially for image saliency prediction. However, these models generally do not consider the dynamic nature of the scenes, and hence, they work better on static ima...

In this study, we propose a novel method to measure bottom-up saliency maps of natural images. In order to eliminate the influence of top-down signals, backward masking is used to make stimuli (natural images) subjectively invisible to subjects, however, the bottom-up saliency can still orient the subjects attention. To measure this orientation/attention effect, we adopt the cueing effect parad...

This paper is to present a Bayesian learning based framework for visual saliency detection in natural scenes. Especially, for any point in the scene, this framework has considered whether it is salient or not; but previous methods by Bayesian learning seem not to do so. This framework includes two steps. First, the framework indicates that visual saliency is constituted with three main saliency...

Visual Saliency is the perceptual quality that makes an object or pixel region stand out relative to its neighbors and thus capture viewer attention. Thus Saliency models indicate the region of interest. This paper aims at improving the performance of sketch based image retrieval using saliency detection approach. Several methods have been developed to extract the saliency information from an i...

A salient image region is defined as an image part that is clearly different from its surround in terms of a number of attributes. In bottom-up processing, these attributes are defined as: contrast, color difference, brightness, and orientation. By measuring these attributes, visual saliency algorithms aim to predict the regions in an image that would attract our attention under free viewing co...

Saliency detection is a useful tool for video-based, real-time Computer Vision applications. It allows to select which locations of the scene are the most relevant and has been used in a number of related assistive technologies such as life-logging, memory augmentation and object detection for the visually impaired, as well as to study autism and the Parkinson’s disease. Many works focusing on ...

The research on computational saliency models has been an active topic in the past decades, due to its broad applications in the fields of multimedia processing, computer vision, cognitive science, robotics, etc. Saliency models mainly target two genres of applications, i.e., human fixation prediction or salient object detection/segmentation. Although recent years have witnessed significant pro...

Recently, saliency detection in a single image and co-saliency detection in multiple images have drawn extensive research interest in the vision community. In this paper, we investigate a new problem of co-saliency detection within a single image, i.e., detecting within-image co-saliency. By identifying common saliency within an image, e.g., highlighting multiple occurrences of an object class ...