The visual saliency detection model simulates the human visual system to perceive the scene and has been widely used in many vision tasks. With the development of acquisition technology, more comprehensive information, such as depth cue, inter-image correspondence, or temporal relationship, is available to extend image saliency detection to RGBD saliency detection, co-saliency detection, or video saliency detection. The RGBD saliency detection model focuses on extracting the salient regions from RGBD images by combining the depth information. The co-saliency detection model introduces the inter-image correspondence constraint to discover the common salient object in an image group. The goal of the video saliency detection model is to locate the motion-related salient object in video sequences, which considers the motion cue and spatiotemporal constraint jointly. In this paper, we review different types of saliency detection algorithms, summarize the important issues of the existing methods, and discuss the existent problems and future works. Moreover, the evaluation datasets and quantitative measurements are briefly introduced, and the experimental analysis and discussion are conducted to provide a holistic overview of different saliency detection methods.

pdf/review-of-visual-saliency-detection-with-comprehensive-4pgvbgehri.pdf

Review of Visual Saliency Detection With Comprehensive Information

With a good balance between tracking accuracy and speed, correlation filter (CF) has become one of the best object tracking frameworks, based on which many successful trackers have been developed. Recently, spatially regularized CF tracking (SRDCF) has been developed to remedy the annoying boundary effects of CF tracking, thus further boosting the tracking performance. However, SRDCF uses a fixed spatial regularization map constructed from a loose bounding box and its performance inevitably degrades when the target or background show significant variations, such as object deformation or occlusion. To address this problem, we propose a new dynamic saliency-aware regularized CF tracking (DSAR-CF) scheme. In DSAR-CF, a simple yet effective energy function, which reflects the object saliency and tracking reliability in the spatial–temporal domain, is defined to guide the online updating of the regularization weight map using an efficient level-set algorithm. Extensive experiments validate that the proposed DSAR-CF leads to better performance in terms of accuracy and speed than the original SRDCF.

Dynamic Saliency-Aware Regularization for Correlation Filter-Based Object Tracking

Co-saliency detection aims to discover common and salient objects in an image group containing more than two relevant images. Moreover, depth information has been demonstrated to be effective for many computer vision tasks. In this paper, we propose a novel co-saliency detection method for RGBD images based on hierarchical sparsity reconstruction and energy function refinement. With the assistance of the intrasaliency map, the inter-image correspondence is formulated as a hierarchical sparsity reconstruction framework. The global sparsity reconstruction model with a ranking scheme focuses on capturing the global characteristics among the whole image group through a common foreground dictionary. The pairwise sparsity reconstruction model aims to explore the corresponding relationship between pairwise images through a set of pairwise dictionaries. In order to improve the intra-image smoothness and inter-image consistency, an energy function refinement model is proposed, which includes the unary data term, spatial smooth term, and holistic consistency term. Experiments on two RGBD co-saliency detection benchmarks demonstrate that the proposed method outperforms the state-of-the-art algorithms both qualitatively and quantitatively.

HSCS: Hierarchical Sparsity Based Co-saliency Detection for RGBD Images

Salient object detection models mimic the behavior of human beings and capture the most salient region/object from the images or scenes, this field contains many important applications in both computer vision and pattern recognition tasks. Despite hundreds of models that have been proposed in this field, but still, it requires a large room for research. This paper demonstrates a detailed overview of the recent progress of saliency detection models in terms of heuristic-based techniques and deep learning-based techniques. we have discussed and reviewed its co-related fields, such as Eye-fixation-prediction, RGBD salient-object-detection, co-saliency object detection, and video-saliency-detection models. We have reviewed the key issues of the current saliency models and discussed future trends and recommendations. The broadly utilized datasets and assessment strategies are additionally investigated in this paper.

/pdf/a-brief-survey-of-visual-saliency-detection-4z4pod107d.pdf

A brief survey of visual saliency detection

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.

Salient Object Detection Techniques in Computer Vision-A Survey.

Cosaliency detects the common salient objects within a group of images. Hence, those objects that are salient only in individual image or small portion of the image group should conceptually be treated as background. However, most state-of-the-art methods cannot do this well because they measure cosaliency as an explicit combination of single-image saliency and interimage similarity, thus inevitably leaving single saliency residuals into the cosaliency maps. In this letter, we show such problem can be solved by color feature reinforcement, based on a simple observation that cosalient objects usually have similar color distributions in an abundant color feature space. Since we model the cosaliency of an image w.r.t. another one as a reinforced product of the foreground dictionary and sparsely coded saliency coefficients of the two images, respectively, within a same rich feature space, we can effectively eliminate the single saliency residual effect in cosaliency detection. Extensive experiments validate the superior performance of the proposed approach on benchmark datasets.

Color Feature Reinforcement for Cosaliency Detection Without Single Saliency Residuals

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Color Feature Reinforcement for Cosaliency Detection Without Single Saliency Residuals