Salient region detection by fusing bottom-up and top-down features extracted from a single image

A Data-Driven and Biologically Inspired Preprocessing Scheme to Improve Visual Object Recognition

Autonomous object recognition in images is one of the most critical topics in security and commercial applications. Due to recent advances in visual neuroscience, the researchers tend to extend biologically plausible schemes to improve the accuracy of object recognition. Preprocessing is one part of the visual recognition system that has received much less attention. In this paper, we propose a new, simple, and biologically inspired pre processing technique by using the data-driven mechanism of visual attention. In this part, the responses of Retinal Ganglion Cells (RGCs) are simulated. After obtaining these responses, an efficient threshold is selected. Then, the points of the raw image with the most information are extracted according to it. Then, the new images with these points are created, and finally, by combining these images with entropy coefficients, the most salient object is located. After extracting appropriate features, the classifier categorizes the initial image into one of the predefined object categories. Our system was evaluated on the Caltech-101 dataset. Experimental results demonstrate the efficacy and effectiveness of this novel method of preprocessing.

Efficient Vanishing Point Detection for Driving Assistance Based on Visual Saliency Map and Image Segmentation from a Vehicle Black-Box Camera

Techniques for detecting a vanishing point (VP) which estimates the direction of a vehicle by analyzing its relationship with surrounding objects have gained considerable attention recently. VPs can be used to support safe vehicle driving in areas such as for autonomous driving, lane-departure avoidance, distance estimation, and road-area detection, by detecting points in which parallel extension lines of objects are concentrated at a single point in a 3D space. In this paper, we proposed a method of detecting the VP in real time for applications to intelligent safe-driving support systems. In order to support safe driving of autonomous vehicles, it is necessary to drive the vehicle with the VP in center of the road image in order to prevent the vehicle from moving out of the road area while driving. Accordingly, in order to detect the VP in the road image, a method of detecting a point where straight lines intersect in an area where edge directional feature information is concentrated is required. The visual attention model and image segmentation process are applied to quickly identify candidate VPs in the area where the edge directional feature-information is concentrated and the intensity contrast difference is large. In the proposed method, VPs are detected by analyzing the edges, visual-attention regions, linear components using the Hough transform, and image segmentation results in an input image. Our experimental results have shown that the proposed method could be applied to safe-driving support systems.

Point Cloud Saliency Detection by Local and Global Feature Fusion

Inspired by the characteristics of the human visual system, a novel method is proposed for detecting the visually salient regions on 3D point clouds. First, the local distinctness of each point is evaluated based on the difference with its local surroundings. Then, the point cloud is decomposed into small clusters, and the initial global rarity value of each cluster is calculated; a random walk ranking method is then used to introduce cluster-level global rarity refinement to each point in all the clusters. Finally, an optimization framework is proposed to integrate both the local distinctness and the global rarity values to obtain the final saliency detection result of the point cloud. We compare the proposed method with several relevant algorithms and apply it to some computer graphics applications, such as interest point detection, viewpoint selection, and mesh simplification. The experimental results demonstrate the superior performance of the proposed method.

Saliency Optimization and Integration Via Iterative Bootstrap Learning

This paper proposes an effective method to elevate the performance of saliency detection via iterative bootstrap learning, which consists of two tasks including saliency optimization and saliency integration. Specifically, first, multiscale segmentation and feature extraction are performed on the input image successively. Second, prior saliency maps are generated using existing saliency models, which are used to generate the initial saliency map. Third, prior maps are fed into the saliency regressor together, where training samples are collected from the prior maps at multiple scales and the random forest regressor is learned from such training data. An integration of the initial saliency map and the output of saliency regressor is deployed to generate the coarse saliency map. Finally, in order to improve the quality of saliency map further, both initial and coarse saliency maps are fed into the saliency regressor together, and then the output of the saliency regressor, the initial saliency map as well as the coarse saliency map are integrated into the final saliency map. Experimental results on three public data sets demonstrate that the proposed method consistently achieves the best performance and significant improvement can be obtained when applying our method to existing saliency models.

Gaze distribution analysis and saliency prediction across age groups

Knowledge of the human visual system helps to develop better computational models of visual attention. State-of-the-art models have been developed to mimic the visual attention system of young adults that, however, largely ignore the variations that occur with age. In this paper, we investigated how visual scene processing changes with age and we propose an age-adapted framework that helps to develop a computational model that can predict saliency across different age groups. Our analysis uncovers how the explorativeness of an observer varies with age, how well saliency maps of an age group agree with fixation points of observers from the same or different age groups, and how age influences the center bias tendency. We analyzed the eye movement behavior of 82 observers belonging to four age groups while they explored visual scenes. Explorative- ness was quantified in terms of the entropy of a saliency map, and area under the curve (AUC) metrics was used to quantify the agreement analysis and the center bias tendency. Analysis results were used to develop age adapted saliency models. Our results suggest that the proposed age-adapted saliency model outperforms existing saliency models in predicting the regions of interest across age groups.

Salient object detection using coarse-to-fine processing

In this paper, we propose a salient object detection algorithm that considers both background and foreground cues. It integrates both coarse salient region extraction and a top-down background weight map measure via boundary label propagation into a unified optimization framework to acquire a refined salient map. The coarse saliency map is additionally fused by three prior components: a local contrast map with greater alignment to physiological law, a global focus prior map, and a global color prior map. During the formation of the background weight map, we first construct an affinity matrix and select nodes existing on the border as labels to represent the background. Then we perform a propagation to generate the regional background weight map. Our proposed model was verified on four benchmark datasets, and the experimental results demonstrate that our method has excellent performance.

Unified Saliency Detection Model Using Color and Texture Features

Saliency detection attracted attention of many researchers and had become a very active area of research. Recently, many saliency detection models have been proposed and achieved excellent performance in various fields. However, most of these models only consider low-level features. This paper proposes a novel saliency detection model using both color and texture features and incorporating higher-level priors. The SLIC superpixel algorithm is applied to form an over-segmentation of the image. Color saliency map and texture saliency map are calculated based on the region contrast method and adaptive weight. Higher-level priors including location prior and color prior are incorporated into the model to achieve a better performance and full resolution saliency map is obtained by using the up-sampling method. Experimental results on three datasets demonstrate that the proposed saliency detection model outperforms the state-of-the-art models.