Salient region detection using patch level and region level image abstractions

In this letter, a novel salient region detection approach is proposed. Firstly, color contrast cue and color distribution cue are computed by exploiting patch level and region level image abstractions in a unified way, where these two cues are fused to compute an initial saliency map. A simple and computationally efficient adaptive saliency refinement approach is applied to suppress saliency of background noises, and to emphasize saliency of objects uniformly. Finally, the saliency map is computed by integrating the refined saliency map with center prior map. In order to compensate different needs in speed/accuracy tradeoff, three variants of the proposed approach are also presented in this letter. The experimental results on a large image dataset show that the proposed approach achieve the best performance over several state-of-the-art approaches

Discovering salient objects from videos using spatiotemporal salient region detection

Detecting salient objects from images and videos has many useful applications in computer vision. In this paper, a novel spatiotemporal salient region detection approach is proposed. The proposed approach computes spatiotemporal saliency by estimating spatial and temporal saliencies separately. The spatial saliency of an image is computed by estimating the color contrast cue and color distribution cue. The estimations of these cues exploit the patch level and region level image abstractions in a unified way. The aforementioned cues are fused to compute an initial spatial saliency map, which is further refined to emphasize saliencies of objects uniformly, and to suppress saliencies of background noises. The final spatial saliency map is computed by integrating the refined saliency map with center prior map. The temporal saliency is computed based on local and global temporal saliencies estimations using patch level optical flow abstractions. Both local and global temporal saliencies are fused to compute the temporal saliency. Finally, spatial and temporal saliencies are integrated to generate a spatiotemporal saliency map. The proposed temporal and spatiotemporal salient region detection approaches are extensively experimented on challenging salient object detection video datasets. The experimental results show that the proposed approaches achieve an improved performance than several state-of-the-art saliency detection approaches. In order to compensate different needs in respect of the speed/accuracy tradeoff, faster variants of the spatial, temporal and spatiotemporal salient region detection approaches are also presented in this paper

Salient Object Detection Techniques in Computer Vision-A Survey.

Detection and localization of regions of images that attract immediate human visual attention is currently an intensive area of research in computer vision. The capability of automatic identification and segmentation of such salient image regions has immediate consequences for applications in the field of computer vision, computer graphics, and multimedia. A large number of salient object detection (SOD) methods have been devised to effectively mimic the capability of the human visual system to detect the salient regions in images. These methods can be broadly categorized into two categories based on their feature engineering mechanism: conventional or deep learning-based. In this survey, most of the influential advances in image-based SOD from both conventional as well as deep learning-based categories have been reviewed in detail. Relevant saliency modeling trends with key issues, core techniques, and the scope for future research work have been discussed in the context of difficulties often faced in salient object detection. Results are presented for various challenging cases for some large-scale public datasets. Different metrics considered for assessment of the performance of state-of-the-art salient object detection models are also covered. Some future directions for SOD are presented towards end

Attentive monitoring of multiple video streams driven by a Bayesian foraging strategy

In this paper we shall consider the problem of deploying attention to subsets of the video streams for collating the most relevant data and information of interest related to a given task. We formalize this monitoring problem as a foraging problem. We propose a probabilistic framework to model observer's attentive behavior as the behavior of a forager. The forager, moment to moment, focuses its attention on the most informative stream/camera, detects interesting objects or activities, or switches to a more profitable stream. The approach proposed here is suitable to be exploited for multi-stream video summarization. Meanwhile, it can serve as a preliminary step for more sophisticated video surveillance, e.g. activity and behavior analysis. Experimental results achieved on the UCR Videoweb Activities Dataset, a publicly available dataset, are presented to illustrate the utility of the proposed technique.Comment: Accepted to IEEE Transactions on Image Processin

Data-Driven Shape Analysis and Processing

Data-driven methods play an increasingly important role in discovering geometric, structural, and semantic relationships between 3D shapes in collections, and applying this analysis to support intelligent modeling, editing, and visualization of geometric data. In contrast to traditional approaches, a key feature of data-driven approaches is that they aggregate information from a collection of shapes to improve the analysis and processing of individual shapes. In addition, they are able to learn models that reason about properties and relationships of shapes without relying on hard-coded rules or explicitly programmed instructions. We provide an overview of the main concepts and components of these techniques, and discuss their application to shape classification, segmentation, matching, reconstruction, modeling and exploration, as well as scene analysis and synthesis, through reviewing the literature and relating the existing works with both qualitative and numerical comparisons. We conclude our report with ideas that can inspire future research in data-driven shape analysis and processing.Comment: 10 pages, 19 figure

Spatiotemporal Saliency Detection: State of Art

Saliency detection has become a very prominent subject for research in recent time. Many techniques has been defined for the saliency detection.In this paper number of techniques has been explained that include the saliency detection from the year 2000 to 2015, almost every technique has been included.all the methods are explained briefly including their advantages and disadvantages. Comparison between various techniques has been done. With the help of table which includes authors name,paper name,year,techniques,algorithms and challenges. A comparison between levels of acceptance rates and accuracy levels are made

Attention mechanism in deep neural networks for computer vision tasks

“Attention mechanism, which is one of the most important algorithms in the deep Learning community, was initially designed in the natural language processing for enhancing the feature representation of key sentence fragments over the context. In recent years, the attention mechanism has been widely adopted in solving computer vision tasks by guiding deep neural networks (DNNs) to focus on specific image features for better understanding the semantic information of the image. However, the attention mechanism is not only capable of helping DNNs understand semantics, but also useful for the feature fusion, visual cue discovering, and temporal information selection, which are seldom researched. In this study, we take the classic attention mechanism a step further by proposing the Semantic Attention Guidance Unit (SAGU) for multi-level feature fusion to tackle the challenging Biomedical Image Segmentation task. Furthermore, we propose a novel framework that consists of (1) Semantic Attention Unit (SAU), which is an advanced version of SAGU for adaptively bringing high-level semantics to mid-level features, (2) Two-level Spatial Attention Module (TSPAM) for discovering multiple visual cues within the image, and (3) Temporal Attention Module (TAM) for temporal information selection to solve the Videobased Person Re-identification task. To validate our newly proposed attention mechanisms, extensive experiments are conducted on challenging datasets. Our methods obtain competitive performance and outperform state-of-the-art methods. Selective publications are also presented in the Appendix”--Abstract, page iii