Framework For Salient Object Detection Research Articles

A Two-Stage Bayesian Integration Framework for Salient Object Detection on Light Field

Unique visual features of 4D light field data have been shown to affect detection of salient objects. Nevertheless, only a few studies explore it yet. In this study, several helpful visual features extracted from light field data are fused in a two-stage Bayesian integration framework for salient object detection. First, background weighted color contrast is computed in high dimensional color space, which is more distinctive to identify object of interest. Second, focusness map of foreground slice is estimated. Then, it is combined with the color contrast results via first-stage Bayesian fusion. Third, background weighted depth contrast is computed. Depth contrast has been proved to be an extremely useful cue for salient object detection and complementary to color contrast. Finally, in the second-stage Bayesian fusion step, the depth-induced contrast saliency is further fused with the first-stage saliency fusion results to get the final saliency map. Experiments of comparing with eight existing state-of-the-art methods on light field benchmark datasets show that the proposed method can handle challenging scenarios such as cluttered background, and achieves the most visually acceptable salient object detection results.

Salient Object Detection Based on Background Feature Clustering

Automatic estimation of salient object without any prior knowledge tends to greatly enhance many computer vision tasks. This paper proposes a novel bottom-up based framework for salient object detection by first modeling background and then separating salient objects from background. We model the background distribution based on feature clustering algorithm, which allows for fully exploiting statistical and structural information of the background. Then a coarse saliency map is generated according to the background distribution. To be more discriminative, the coarse saliency map is enhanced by a two-step refinement which is composed of edge-preserving element-level filtering and upsampling based on geodesic distance. We provide an extensive evaluation and show that our proposed method performs favorably against other outstanding methods on two most commonly used datasets. Most importantly, the proposed approach is demonstrated to be more effective in highlighting the salient object uniformly and robust to background noise.

A novel graph-based optimization framework for salient object detection

In traditional graph-based optimization framework for salient object detection, an image is over-segmented into superpixels and mapped to one single graph. The saliency value of each superpixel is then computed based on the similarity between connected nodes and the saliency related queries. When applying the traditional graph-based optimization framework to the salient object detection problem in natural scene images, we observe at least two limitations: only one graph is employed to describe the information contained in an image and no cognitive property about visual saliency is explicitly modeled in the optimization framework. In this work, we propose a novel graph-based optimization framework for salient object detection. Firstly, we employ multiple graphs in our optimization framework. A natural scene image is usually complex, employing multiple graphs from different image properties can better describe the complex information contained in the image. Secondly, we model one popular cognitive property about visual saliency (visual rarity) in our graph-based optimization framework, making this framework more suitable for saliency detection problem. Specifically, we add a regularization term to constrain the saliency value of each superpixel according to visual rarity in our optimization framework. Our experimental results on four benchmark databases with comparisons to fifteen representative methods demonstrate that our graph-based optimization framework is effective and computationally efficient.

A Universal Framework for Salient Object Detection

In this paper, we propose a novel universal framework for salient object detection, which aims to enhance the performance of any existing saliency detection method. First, rough salient regions are extracted from any existing saliency detection model with distance weighting, adaptive binarization, and morphological closing. With the superpixel segmentation, a Bayesian decision model is adopted to refine the rough saliency map to obtain a more accurate saliency map. An iterative optimization method is designed to obtain better saliency results by exploiting the characteristics of the output saliency map each time. Through the iterative optimization process, the rough saliency map is updated step by step with better and better performance until an optimal saliency map is obtained. Experimental results on the public salient object detection datasets with ground truth demonstrate the promising performance of the proposed universal framework subjectively and objectively.

A novel optimization framework for salient object detection

Visual saliency aims to locate the noticeable regions or objects in an image. In this paper, a coarse-to-fine measure is developed to model visual saliency. In the proposed approach, we firstly use the contrast and center bias to generate an initial prior map. Then, we weight the initial prior map with boundary contrast to obtain the coarse saliency map. Finally, a novel optimization framework that combines the coarse saliency map, the boundary contrast and the smoothness prior is introduced with the intention of refining the map. Experiments on three public datasets demonstrate the effectiveness of the proposed method.