Visual Saliency Detection Model Research Articles

Ship Detection in Visible Remote Sensing Image Based on Saliency Extraction and Modified Channel Features

Ship detection in visible remote sensing (VRS) images has been widely used in the military and civil fields. However, the various backgrounds and the variable scale and orientation bring great difficulties to effective detection. In this paper, we propose a novel ship target detection scheme based on small training samples. The scheme contains two main stages: candidate region extraction and ship identification. In the first stage, we propose a visual saliency detection model based on the difference in covariance statistical characteristics to quickly locate potential ships. Moreover, the multi-scale fusion for the saliency model is designed to overcome the problem of scale variation. In the second stage, we propose a three-channel aggregate feature, which combines a rotation-invariant histogram of oriented gradient and the circular frequency feature. The feature can identify the ship target well by avoiding the impact of its rotation and shift. Finally, we propose the VRS ship dataset that contains more realistic scenes. The results on the VRS ship dataset demonstrate that the saliency model achieves the best AUC value with 0.9476, and the overall detection achieves a better performance of 65.37% in terms of AP@0.5:0.95, which basically meets the need of the detection tasks.

A spatial-frequency-temporal domain based saliency model for low contrast video sequences

The last few decades have witnessed rapid development of visual saliency detection, as it can detect object-of-interest from clutter environments to substantially facilitate a wide range of applications. However, traditional visual saliency detection models primarily rely on image features, which may face great challenges in low contrast video stream captured from low lighting scenarios. This paper proposes a dynamic multimodal fusion based visual saliency detection model towards low contrast videos, which combines saliency information from spatial, frequency, and temporal domains. In spatial domain, superpixel covariance is utilized to compute the region dissimilarity under low lighting scenarios; in frequency domain, the amplitude spectrum tuned method is used to suppress the background noise; in temporal domain, the incremental learning is employed to efficiently update background model from high dimensional video streams. Extensive experiments have been conducted to validate the effectiveness of the proposed model.

Review of Visual Saliency Detection With Comprehensive Information

Visual saliency detection model simulates the human visual system to perceive the scene, and has been widely used in many vision tasks. With the acquisition technology development, 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. RGBD saliency detection model focuses on extracting the salient regions from RGBD images by combining the depth information. Co-saliency detection model introduces the inter-image correspondence constraint to discover the common salient object in an image group. The goal of 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 discission are conducted to provide a holistic overview of different saliency detection methods.

An efficient visual saliency detection model based on Ripplet transform

Even though there have been great advancements in computer vision tasks, the development of human visual attention models is still not well investigated. In day-to-day life, one can find ample applications of saliency detection in image and video processing. This paper presents an efficient visual saliency detection model based on Ripplet transform, which aims at detecting the salient region and achieving higher Receiver Operating Characteristics (ROC). Initially the feature maps are obtained from Ripplet transform in different scales and different directions of the image. The global and local saliency maps are computed based on the global probability density distribution and feature distribution of local areas, which are combined together to get the final saliency map. Ripplet-transform-based visual saliency detection is the novel approach carried out in this paper. Experimental results indicate that the proposed method based on Ripplet transformation can give excellent performance in terms of precision, recall, F measure and Mean Absolute Error (MAE), and is compared with 10 state-of-the-art methods on five benchmark datasets.

Using Saliency-Weighted Disparity Statistics for Objective Visual Comfort Assessment of Stereoscopic Images

Visual comfort assessment (VCA) for stereoscopic images is a particularly significant yet challenging task in 3D quality of experience research field. Although the subjective assessment given by human observers is known as the most reliable way to evaluate the experienced visual discomfort, it is time-consuming and non-systematic. Therefore, it is of great importance to develop objective VCA approaches that can faithfully predict the degree of visual discomfort as human beings do. In this paper, a novel two-stage objective VCA framework is proposed. The main contribution of this study is that the important visual attention mechanism of human visual system is incorporated for visual comfort-aware feature extraction. Specifically, in the first stage, we first construct an adaptive 3D visual saliency detection model to derive saliency map of a stereoscopic image, and then a set of saliency-weighted disparity statistics are computed and combined to form a single feature vector to represent a stereoscopic image in terms of visual comfort. In the second stage, a high dimensional feature vector is fused into a single visual comfort score by performing random forest algorithm. Experimental results on two benchmark databases confirm the superior performance of the proposed approach.

Perception-oriented video saliency detection via spatio-temporal attention analysis

Human visual system actively seeks salient regions and movements in video sequences to reduce the search effort. Computational visual saliency detection model provides important information for semantic understanding in many real world applications. In this paper, we propose a novel perception-oriented video saliency detection model to detect the attended regions for both interesting objects and dominant motions in video sequences. Based on the visual orientation inhomogeneity of human perception, a novel spatial saliency detection technique called visual orientation inhomogeneous saliency model is proposed. In temporal saliency detection, a novel optical flow model is created based on the dynamic consistency of motion. We fused the spatial and the temporal saliency maps together to build the spatio-temporal attention analysis model toward a uniform framework. The proposed model is evaluated on three typical video datasets with six visual saliency detection algorithms and achieves remarkable performance. Empirical validations demonstrate the salient regions detected by the proposed model highlight the dominant and interesting objects effectively and efficiently. More importantly, the saliency regions detected by the proposed model are consistent with human subjective eye tracking data.

3D visual saliency detection model with generated disparity map

Due to the remarkable distinction between human monocular vision and binocular vision, stereoscopic visual attention becomes an emerging question in the study of 3D applications. Some of existing 3D visual saliency detection models take advantage of ground-truth disparity map to compute center-surround differences of the depth features with high computational cost. In some 3D applications, the ground-truth disparity map may not be always available. In this paper, an efficient and simple 3D visual saliency detection model is proposed without using ground-truth disparity map. The proposed model is based on a band-pass filtering method which coincides with the visual perceptual process in human visual system. Firstly, the monocular luminance, color and texture features are extracted from the left view's image; the binocular depth feature is extracted from the two views' disparity map. Then, all the feature maps are filtered to generate three types of saliency maps, i.e., 2D saliency map, texture saliency map and depth saliency map. Subsequently, the three saliency maps are fused to one 3D saliency map by a linear pooling strategy. Finally, the final 3D visual saliency map is enhanced by the center-bias factor. Experimental results on a public eye tracking database show that the proposed model achieves better detection performance with low computational cost among the existing 3D visual saliency detection models.

A Novel Target-Objected Visual Saliency Detection Model in Optical Satellite Images

A target-oriented visual saliency detection model for optical satellite images is proposed in this paper. This model simulates the structure of the human vision system and provides a feasible way to integrate top-down and bottom-up mechanism in visual saliency detection. Firstly, low-level visual features are extracted to generate a low-level visual saliency map. After that, an attention shift and selection process is conducted on the low-level saliency map to find the current attention region. Lastly, the original version of hierarchical temporal memory (HTM) model is optimized to calculate the target probability of the attention region. The probability is then fed back to the low-level saliency map in order to obtain the final target-oriented high-level saliency map. The experiment for detecting harbor targets was performed on the real optical satellite images. Experimental results demonstrate that, compared with the purely bottom-up saliency model and the VOCUS top-down saliency model, our model significantly improves the detection accuracy