Humans and other primates shift their gaze to allocate processing resources to a subset of the visual input. Understanding and emulating the way that human observers free-view a natural scene has both scientific and economic impact. While previous research focused on low-level image features in saliency, the problem of “semantic gap” has recently attracted attention from vision researchers, and...

In this paper, we present a tracking technique utilizing a simple saliency visual descriptor. Initially, we define a visual descriptor named local similarity pattern that mimics the famous texture operator local binary patterns. The key difference is that it assigns each pixel a code based on the similarity to the neighbouring pixels. Later, we simplify this descriptor to ...

Much research has been concerned with the contribution of the low level features of a visual scene to the deployment of visual attention. Bottom-up saliency models have been developed to predict the location of gaze according to these features. So far, color besides to brightness, contrast and motion is considered as one of the primary features in computing bottom-up saliency. However, its cont...

Attention Restoration Theory (ART) states that built scenes place greater load on attentional resources than natural scenes. This is explained in terms of "hard" and "soft" fascination of built and natural scenes. Given a lack of direct empirical evidence for this assumption we propose that perceptual saliency of scene content can function as an empirically derived indicator of fascination. Sal...

The purpose of this paper is to demonstrate a perceptually based spatio-temporal computational framework for visual saliency estimation. We have developed a new spatiotemporal visual frontend based on biologically inspired 3D Gabor filters, which is applied on both the luminance and the color streams and produces spatio-temporal energy maps. These volumes are fused for computing a single salien...

Saliency detection plays an important role in image segmentation, object detection and retrieval, which attracts more attention in the field of computer vision recently. Most existing saliency detection algorithms have not considered the influence of visual focus shifting yet. In this paper, a novel algorithm named moving region contrast (MRC) is proposed to analyze image saliency. The algorith...

• Employs three kind of low-level contrast features, including color, intensity and orientation, and the center prior knowledge to introduce saliency prior maps. • Train the RFCN with two stage training strategy, pre-training on the segmentation data set and fine-tuning on the saliency data set. The recurrent structure can incorporate the saliency prior maps into the CNNs with an end-to-end tra...

Saliency mechanisms play an important role when visual recognition must be performed in cluttered scenes. We propose a computational definition of saliency that deviates from existing models by equating saliency to discrimination. In particular, the salient attributes of a given visual class are defined as the features that enable best discrimination between that class and all other classes of ...

We present a system for automatically detecting salient image regions in stereoscopic videos. This report extends our previous system [4] and provides additional details about its implementation. Our proposed algorithm considers information based on three dimensions: salient colors in individual frames, salient information derived from camera and object motion, and depth saliency. These three c...

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...