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-...

In this paper, we present a novel approach to saliency detection. The method we propose here aims at synthesizing common knowledge of saliency in an image. We define a visually salient region with following two parameters; the spatial redundancy and its local appearance. The former is its probability of occurrence within the image, which is a quantification of the " rarity " of the concerned re...

In this paper, we present an image saliency detection method by constructing graph model. We extract color, texture and compactness features and segment superpixels from an input image to construct a graph model. Then, saliency for each is measured by calculating random walker probability on the node. Extensive results on MSRA dataset containing 1000 test images with ground truths demonstrate t...

Smoke detection is the key to the early warning of the fire, and it is hard to reach the unified standard of the smoke detection because of different environments and different combustions. Considering the continuity of the occurrence of smoke and the more obvious visual saliency along with the long-time integration, the paper proposes the algorithm of multi-step accumulation of inter-frame dif...

Abstract: Saliency map is an important and very useful representation of visually important parts of the image. It highlights the salient parts of the image by limiting the visual information coming to the human visual system (HVS). Many models have been proposed in the literature for computing the saliency maps. All of them are having their own pros and cons. No model is perfect. But it has be...

Visual saliency detection aims at identifying the most visually distinctive parts in an image, and serves as a pre-processing step for a variety of computer vision and image processing tasks. To this end, the saliency detection procedure must be as fast and compact as possible and optimally processes input images in a real time manner. It is an essential application requirement for the saliency...

Salient objects detection aims to locate objects that capture human attention within images. Recent progresses in saliency detection have exploited the center prior, to combine with other cues such as background information, object size or region contrast, achieving competitive results. However, previous approaches of center prior supposing salient object locates nearly at image center is very ...

Detecting salient objects from a video requires exploiting both spatial and temporal knowledge included in the video. We propose a novel region-based multiscale spatiotemporal saliency detection method for videos, where static features and dynamic features computed from the low and middle levels are combined together. Our method utilizes such combined features spatially over each frame and, at ...

Object detection in a huge volume of aerial imagery requires first detecting the salient regions. When an image is over-segmented by the superpixels, the latters will adhere to object boundaries, resulting in their shape deformation and size variation, which can be used as the saliency measure. The normalized Hausdorff distances from the inner pixels to boundary of the superpixels are then tran...

Saliency detection means detecting visually attractive regions in images. It is an aspect of exploring visual attention from a computer vision. Each image is segmented to get bags. Features are extracted from each bag. Features, including low-, mid-,and high-level, are incorporated into the learning and testing process. They are position, color, texture, scale, center prior, and boundary. From ...