U-FIN: Unsupervised Feature Integration Approach for Salient Object Detection

2020 ◽  
pp. 1173-1188
Author(s):  
Vivek Kumar Singh ◽  
Nitin Kumar
Keyword(s):  
Object Detection ◽  
Salient Object Detection ◽  
Feature Integration ◽  
Salient Object ◽  
Integration Approach

scholarly journals Salient Object Detection: A Discriminative Regional Feature Integration Approach

2016 ◽  
Vol 123 (2) ◽  
pp. 251-268 ◽  
Author(s):  
Jingdong Wang ◽  
Huaizu Jiang ◽  
Zejian Yuan ◽  
Ming-Ming Cheng ◽  
Xiaowei Hu ◽  
...  
Keyword(s):  
Object Detection ◽  
Salient Object Detection ◽  
Feature Integration ◽  
Salient Object ◽  
Regional Feature ◽  
Integration Approach

RGBD salient object detection based on regional feature integration

2016 ◽  
Vol 31 (1) ◽  
pp. 117-123
Author(s):  
杜 杰 DU Jie ◽  
吴 谨 WU Jin ◽  
朱 磊 ZHU Lei
Keyword(s):  
Object Detection ◽  
Salient Object Detection ◽  
Feature Integration ◽  
Salient Object ◽  
Regional Feature

scholarly journals Accurate salient object detection via dense recurrent connections and residual-based hierarchical feature integration

2019 ◽  
Vol 78 ◽  
pp. 103-112 ◽  
Author(s):  
Yanpeng Cao ◽  
Guizhong Fu ◽  
Jiangxin Yang ◽  
Yanlong Cao ◽  
Michael Ying Yang
Keyword(s):  
Object Detection ◽  
Salient Object Detection ◽  
Feature Integration ◽  
Salient Object ◽  
Recurrent Connections

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.

scholarly journals Label Decoupling Framework for Salient Object Detection

2020 ◽  
Author(s):  
Jun Wei ◽  
Shuhui Wang ◽  
Zhe Wu ◽  
Chi Su ◽  
Qingming Huang ◽  
...  
Keyword(s):  
Object Detection ◽  
Salient Object Detection ◽  
Salient Object
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