Saliency maps as visual attention computational models can reveal novel regions within a scene (as in the human visual system), which can decrease the amount of data to be processed in task specific computer vision applications. Most of the saliency computation models do not take advantage of prior spatial memory by giving priority to spatial or object based features to obtain bottom-up or top-...

A new framework is presented for saliency based analysis of naturally complex scenes. In this framework, the fluctuation of local scale shift and the diversity of subtle chromatic scattering are extracted as the environmental saliency: viewer specific visualization of sign patterns to be associated with landmark objects. Being guided by the probability distribution of the scale shift, the scene...

A model connecting visual tracking and saliency has recently been proposed. This model is based on the saliency hypothesis for tracking which postulates that tracking is achieved by the top-down tuning, based on target features, of discriminant center-surround saliency mechanisms over time. In this work, we identify three main predictions that must hold if the hypothesis were true: 1) tracking ...

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...

We propose a novel image retrieval framework for visual saliency detection using information about salient objects contained within bounding box annotations for similar images. For each test image, we train a customized SVM from similar example images to predict the saliency values of its object proposals and generate an external saliency map (ES) by aggregating the regional scores. To overcome...

The quantification of object saliency in 2D greyscale images is of primary importance in medical imaging. Saliency, in this paper, refers to the detectability of objects of unknown location, an object being a feature of interest in an image. Object saliency is commonly measured by the speed of an observer at performing a detection task. The more salient an object is, the more quickly it can be ...

Image Quality Assessment (IQA) is an interesting challenge for image processing applications. The goal of IQA is to replace human judgement of perceived image quality with a machine evaluation. A large number of methods have been proposed to evaluate the quality of an image which may be corrupted by noise, distorted during acquisition, transmission, compression, etc. Many methods, in some cases...

There is growing interest in studying the Human Visual System (HVS) to supplement and improve the performance of computer vision tasks. A major challenge for current visual saliency models is predicting saliency in cluttered scenes (i.e. high false positive rate). In this paper, we propose a fixation patch detector that predicts image patches that contain human fixations with high probability. ...

Visual context is used in different forms for saliency computation. While its use in saliency models for fixations prediction is often reasoned, this is less so the case for approaches that aim to compute saliency at the object level. We argue that the types of context employed by these methods lack clear justification and may in fact interfere with the purpose of capturing the saliency of whol...

Image salient object detection is useful for applications like image retrieval, object recognition etc. Aiming to obtain the pixel-level saliency map, most traditional saliency models ignore the object-level information and become inappropriate in complex scenes. A new salient object detection method is proposed in this paper by incorporating the notion of object and background prior directly i...