Visual saliency, which distinguishes “interesting” visual content from others, plays an important role in multimedia and computer vision applications. This chapter starts with a brief overview of visual saliency as well as the literature of some popular models to detect salient regions. We describe two methods to model visual saliency – one in images and the other in videos. Specifically, we in...

Visual saliency is a computational process that seeks to identify the most attention-drawing regions from a visual point of view. Most methods of salience are based on characteristics such as color, texture and more recently tried to introduce the depth information, which is known to be an important cue in the human cognitive system. We present a new full 3D mechanism of computational attention...

Saliency is the perceptual capacity of our visual system to focus attention (i.e. gaze) on relevant objects instead background. So far, computational methods for saliency estimation required explicit generation a map, process which usually achieved via eyetracking experiments still images. This tedious that needs be repeated each new dataset. In current paper, we demonstrate possible automatica...

People perform a remarkable range of tasks that require search of the visual environment for a target item among distractors. The Guided Search model (Wolfe, 1994, 2007), or GS, is perhaps the best developed psychological account of human visual search. To prioritize search, GS assigns saliency to locations in the visual field. Saliency is a linear combination of activations from retinotopic ma...

Early visual processing as a method to speed up computations on visual input data has long been discussed in the computer vision community. The general target of a such approaches is to filter nonrelevant information from the costly higher-level visual processing algorithms. By insertion of this additional filter layer the overall approach can be speeded up without actually changing the visual ...

In this paper, a method of constructing saliency map based on learning method is proposed. The saliency map is constructed by nonlinear combination of features, and the nonlinear function is approached through a feed forward artificial neutral network (ANN) whose weight is trained by examples. The efficiency of the proposed method is verified in the eye detection problem. Experiments show that ...

The saliency map is a computational model and has been constructed for simulating human saliency processing, e.g. pop-out target detection (e.g. Itti & Koch, 2000). In this study the spatial structure on the saliency map was investigated. It is proposed that the saliency map is structured into processing units whose size is increasing with retinal eccentricity. In two experiments the distance b...

In many real-world tasks, the ability to focus attention on the important features of the input is crucial for good performance. In this paper a mechanism for achieving task-specific focus of attention is presented. A saliency map, which is based upon a computed expectation of the contents of the inputs at the next time step, indicates which regions of the input retina are important for perform...

This paper proposes an unsupervised bottom-up saliency detection approach by aggregating complementary background template with refinement. Feature vectors are extracted from each superpixel to cover regional color, contrast and texture information. By using these features, a coarse detection for salient region is realized based on background template achieved by different combinations of bound...

In many medical image segmentation applications identifying and extracting the region of interest (ROI) accurately is an important step. The usual approach to extract ROI is to apply image segmentation methods. In this paper, we focus on extracting ROI by segmentation based on visual attended locations. Chan-Vese active contour model is used for image segmentation and attended locations are det...