In this paper, a novel saliency theory based RR-IQA metric is introduced. As the human visual system is sensitive to the salient region, evaluating the image quality based on the salient region could increase the accuracy of the algorithm. In order to extract the salient regions, we use blob decomposition (BD) tool as a texture component descriptor. A new method for blob decomposition is propos...

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...

This paper presents a spatiotemporal saliency visual attention model based on maximum entropy. A dynamic saliency map is created by calculating entropies in every corresponding local region between the current and some previous frames in a short video. In the same time, four low-level visual features including color contrast, intensity contrast, orientation and texture, are extracted and are co...

Abstract Saliency detection mimics the natural visual attention mechanism that identifies an imagery region to be salient when it attracts more than background. This image analysis task covers many important applications in several fields such as military science, ocean research, resources exploration, disaster and land-use monitoring tasks. Despite hundreds of models have been proposed for sal...

The paper contributes to No-Reference video quality assessment of broadcasted HD video over IP networks and DVB. In this work we have enhanced our bottom-up spatio-temporal saliency map model by considering semantics of the visual scene. Thus we propose a new saliency map model based on face detection that we called semantic saliency map. A new fusion method has been proposed to merge the botto...

The human visual system can quickly and efficiently capture the salient objects in a scene. Based on the biological mechanism, a new multi-scale saliency analysis method is proposed in this paper, in which the differences of region colors and spaces are calculated in different scale and their saliency map are fused together. First, we calculate the image saliency by using the color and space in...

In an effort to detect the region-of-interest (ROI) of remote sensing images with complex data distributions, sparse representation based on dictionary learning has been utilized, and has proved able to process high dimensional data adaptively and efficiently. In this paper, a visual attention model uniting hyperparameter sparse representation with energy distribution optimization is proposed f...

The human visual system has a remarkable ability to automatically attend to only salient regions, known as focus of attention in complex scenes. This ability enables us to allocate limited perceptual and cognitive resources on task relevant visual input. It is well known that attention also plays an important role when humans perceive visual quality of images. The goal of machine vision systems...

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 ...

In digital photography, two or more objects of a scene cannot be focused at the same time. If we focus one object, we may lose information about other objects and vice versa. Multi-focus image fusion is a process of generating an all-in-focus image from several out-of-focus images. In this paper, we propose a new multi-focus image fusion method based on two-scale image decomposition and salienc...