Saliency map detection, as a method for detecting important regions of an image, is used in many applications such as image classification and recognition. We propose that context detection could have an essential role in image saliency detection. This requires extraction of high level features. In this paper a saliency map is proposed, based on image context detection using semantic segmentati...

I propose that perceptual learning of tasks to detect targets among uniform background items involves changing intra-cortical interactions in the primary visual cortex (V1). This is the case for tasks that rely mainly on bottom-up saliency to guide attention to the task relevant locations quickly, and rely less on top-down knowledge of the stimuli or on other strategies. In particular, suppress...

Video quality assessment (VQA) is very important in many video processing applications. For example, the rate-distortion (RD) optimization in video coding needs an efficient distortion metric to assess the RD cost of candidate coding parameters. However, most existing metrics employ little visual perceptual information, or some are too complex to meet real-time requirement. In this paper we pro...

Eye tracking has become the de facto standard measure of visual attention in tasks that range from free viewing to complex daily activities. In particular, saliency models are often evaluated by their ability to predict human gaze patterns. However, fixations are not only influenced by bottom-up saliency (computed by the models), but also by many top-down factors. Thus, comparing bottom-up sali...

This paper proposes a novel top-down visual saliency detection method for optical satellite images using local adaptive regression kernels. This method provides a saliency map by measuring the likeness of image patches to a given single template image. The local adaptive regression kernel (LARK) is used as a descriptor to extract feature and compare against analogous feature from the target ima...

In the heart of the computer model of visual attention, an interest or saliency map is derived from an input image in a process that encompasses several data combination steps. While several combination strategies are possible and the choice of a method influences the final saliency substantially, there is a real need for a performance comparison for the purpose of model improvement. This paper...

Effectiveness of local binary pattern (LBP) for face recognition has been proven. But the weight of weighted LBP is difficult to determine. In this paper, we proposed a biologically plausible approach to set the weight automatically. Combining LBP and visual attention, a weight map can be constructed by summing over the saliency map. The weight map outlines salient information in the image and ...

Top-down saliency models produce a probability map that peaks at target locations specified by a task/goal such as object detection. They are usually trained in a fully supervised setting involving pixel-level annotations of objects. We propose a weakly supervised top-down saliency framework using only binary labels that indicate the presence/absence of an object in an image. First, the probabi...

Object detection in synthetic aperture radar (SAR) images, which is a fundamental but 10 challenging problem in the field of satellite image interpretation, plays an important role for a wide 11 range of applications and is receiving significant attention in recent years. Recently, the ability of 12 human visual system to detect targets with visual saliency is extraordinarily fast and reliable....

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...