Saliency Detection via Foreground and Background Seeds

2017 ◽  
pp. 145-154 ◽  
Author(s):  
Xiao Lin ◽  
Zhixun Yan ◽  
Linhua Jiang
Keyword(s):  
Saliency Detection

A Universal Descent Model for Saliency Detection

2019 ◽  
Author(s):  
Han Liu ◽  
Bo Li ◽  
Tao Zheng ◽  
Jiaxu Yao
Keyword(s):  
Saliency Detection

Saliency detection in deep learning era: trends of development

2019 ◽  
pp. 10-36 ◽  
Author(s):  
M. N. Favorskaya ◽  
L. C. Jain
Keyword(s):  
Deep Learning ◽  
Object Detection ◽  
Event Detection ◽  
Visual Analysis ◽  
Saliency Detection ◽  
Salient Object Detection ◽  
Public Image ◽  
Detection Methods ◽  
Salient Object ◽  
Salient Event

Introduction:Saliency detection is a fundamental task of computer vision. Its ultimate aim is to localize the objects of interest that grab human visual attention with respect to the rest of the image. A great variety of saliency models based on different approaches was developed since 1990s. In recent years, the saliency detection has become one of actively studied topic in the theory of Convolutional Neural Network (CNN). Many original decisions using CNNs were proposed for salient object detection and, even, event detection.Purpose:A detailed survey of saliency detection methods in deep learning era allows to understand the current possibilities of CNN approach for visual analysis conducted by the human eyes’ tracking and digital image processing.Results:A survey reflects the recent advances in saliency detection using CNNs. Different models available in literature, such as static and dynamic 2D CNNs for salient object detection and 3D CNNs for salient event detection are discussed in the chronological order. It is worth noting that automatic salient event detection in durable videos became possible using the recently appeared 3D CNN combining with 2D CNN for salient audio detection. Also in this article, we have presented a short description of public image and video datasets with annotated salient objects or events, as well as the often used metrics for the results’ evaluation.Practical relevance:This survey is considered as a contribution in the study of rapidly developed deep learning methods with respect to the saliency detection in the images and videos.

Saliency Detection Based on Improved Manifold Ranking via Convex Hull

2019 ◽  
Vol 31 (5) ◽  
pp. 761
Author(s):  
Xiao Lin ◽  
Zuxiang Liu ◽  
Xiaomei Zheng ◽  
Jifeng Huang ◽  
Lizhuang Ma
Keyword(s):  
Convex Hull ◽  
Saliency Detection ◽  
Manifold Ranking

Visual Saliency Detection: An Information Theoretic Algorithm Combined Long-term with Short-term Features

2014 ◽  
Vol 35 (7) ◽  
pp. 1636-1643
Author(s):  
Xiao-liang Qian ◽  
Lei Guo ◽  
Jun-wei Han ◽  
Xin-tao Hu ◽  
Gong Cheng
Keyword(s):  
Saliency Detection ◽  
Visual Saliency ◽  
Short Term ◽  
Information Theoretic ◽  
Visual Saliency Detection

scholarly journals Superpixel Segmentation Based on Anisotropic Edge Strength

2019 ◽  
Vol 5 (6) ◽  
pp. 57 ◽  
Author(s):  
Gang Wang ◽  
Bernard De Baets
Keyword(s):  
Distance Measure ◽  
Saliency Detection ◽  
Experimental Results ◽  
Segmentation Method ◽  
Superpixel Segmentation ◽  
Gaussian Kernels ◽  
First Derivative ◽  
Edge Strength

Superpixel segmentation can benefit from the use of an appropriate method to measure edge strength. In this paper, we present such a method based on the first derivative of anisotropic Gaussian kernels. The kernels can capture the position, direction, prominence, and scale of the edge to be detected. We incorporate the anisotropic edge strength into the distance measure between neighboring superpixels, thereby improving the performance of an existing graph-based superpixel segmentation method. Experimental results validate the superiority of our method in generating superpixels over the competing methods. It is also illustrated that the proposed superpixel segmentation method can facilitate subsequent saliency detection.

Two-stream Encoder GAN with Progressive Training for Co-Saliency Detection

2021 ◽  
pp. 1-1
Author(s):  
Xiaoliang Qian ◽  
Xi Cheng ◽  
Gong Cheng ◽  
Xiwen Yao ◽  
Liying Jiang
Keyword(s):  
Saliency Detection

scholarly journals On Combining DeepSnake and Global Saliency for Detection of Orchard Apples

2021 ◽  
Vol 11 (14) ◽  
pp. 6269
Author(s):  
Wang Jing ◽  
Wang Leqi ◽  
Han Yanling ◽  
Zhang Yun ◽  
Zhou Ruyan
Keyword(s):  
Saliency Detection ◽  
Color Difference ◽  
Saliency Map ◽  
Apple Fruit ◽  
Initial Contour ◽  
Detection Accuracy ◽  
Natural Environments ◽  
Fast Detection ◽  
Single Target ◽  
Training Samples

For the fast detection and recognition of apple fruit targets, based on the real-time DeepSnake deep learning instance segmentation model, this paper provided an algorithm basis for the practical application and promotion of apple picking robots. Since the initial detection results have an important impact on the subsequent edge prediction, this paper proposed an automatic detection method for apple fruit targets in natural environments based on saliency detection and traditional color difference methods. Combined with the original image, the histogram backprojection algorithm was used to further optimize the salient image results. A dynamic adaptive overlapping target separation algorithm was proposed to locate the single target fruit and further to determine the initial contour for DeepSnake, in view of the possible overlapping fruit regions in the saliency map. Finally, the target fruit was labeled based on the segmentation results of the examples. In the experiment, 300 training datasets were used to train the DeepSnake model, and the self-built dataset containing 1036 pictures of apples in various situations under natural environment was tested. The detection accuracy of target fruits under non-overlapping shaded fruits, overlapping fruits, shaded branches and leaves, and poor illumination conditions were 99.12%, 94.78%, 90.71%, and 94.46% respectively. The comprehensive detection accuracy was 95.66%, and the average processing time was 0.42 s in 1036 test images, which showed that the proposed algorithm can effectively separate the overlapping fruits through a not-very-large training samples and realize the rapid and accurate detection of apple targets.

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