Unsupervised Feature Selection for Salient Object Detection

2011 ◽  
pp. 15-26 ◽  
Author(s):  
Viswanath Gopalakrishnan ◽  
Yiqun Hu ◽  
Deepu Rajan
Keyword(s):  
Feature Selection ◽  
Object Detection ◽  
Salient Object Detection ◽  
Salient Object ◽  
Unsupervised Feature Selection ◽  
Selection For

scholarly journals Saliency Detection in Weak Light Images via Optimal Feature Selection-Guided Seed Propagation

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Nan Mu ◽  
Hongyu Wang ◽  
Yu Zhang ◽  
Hongyu Han ◽  
Jun Yang
Keyword(s):  
Feature Selection ◽  
Object Detection ◽  
Saliency Map ◽  
Salient Object Detection ◽  
Propagation Model ◽  
Salient Object ◽  
Optimal Feature Selection ◽  
Weak Light ◽  
Seed Propagation ◽  
Optimal Feature

Salient object detection has a wide range of applications in computer vision tasks. Although tremendous progress has been made in recent decades, the weak light image still poses formidable challenges to current saliency models due to its low illumination and low signal-to-noise ratio properties. Traditional hand-crafted features inevitably encounter great difficulties in handling images with weak light backgrounds, while most of the high-level features are unfavorable to highlight visually salient objects in weak light images. In allusion to these problems, an optimal feature selection-guided saliency seed propagation model is proposed for salient object detection in weak light images. The main idea of this paper is to hierarchically refine the saliency map by learning the optimal saliency seeds in weak light images recursively. Particularly, multiscale superpixel segmentation and entropy-based optimal feature selection are first introduced to suppress the background interference. The initial saliency map is then obtained by the calculation of global contrast and spatial relationship. Moreover, local fitness and global fitness are used to optimize the prediction saliency map. Extensive experiments on six datasets show that our saliency model outperforms 20 state-of-the-art models in terms of popular evaluation criteria.

Discriminative Unimodal Feature Selection and Fusion for RGB-D Salient Object Detection

2021 ◽  
pp. 108359
Author(s):  
Nianchang Huang ◽  
Yongjiang Luo ◽  
Qiang Zhang ◽  
Jungong Han
Keyword(s):  
Feature Selection ◽  
Object Detection ◽  
Salient Object Detection ◽  
Salient Object

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

scholarly journals Multi-Interactive Dual-Decoder for RGB-Thermal Salient Object Detection

2021 ◽  
pp. 1-1
Author(s):  
Zhengzheng Tu ◽  
Zhun Li ◽  
Chenglong Li ◽  
Yang Lang ◽  
Jin Tang
Keyword(s):  
Object Detection ◽  
Salient Object Detection ◽  
Salient Object
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