scholarly journals Salient Object Detection Based on Weighted Hypergraph and Random Walk

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
WeiYi Wei ◽  
Hui Chen
Keyword(s):  
Random Walk ◽  
Object Detection ◽  
Clustering Algorithm ◽  
Graph Model ◽  
Input Image ◽  
Salient Object Detection ◽  
Salient Object ◽  
Hypergraph Model ◽  
Benchmark Datasets ◽  
Weighted Hypergraph

Recently, salient object detection based on the graph model has attracted extensive research interest in computer vision because the graph model can represent the relationship between two regions better. However, it is difficult to capture the high-level relationship between multiple regions. In this algorithm, the input image is segmented into superpixels first. Then, a weighted hypergraph model is established using fuzzy C-means clustering algorithm and a new weighting strategy. Finally, the random walk algorithm is used to sort all superpixels on the weighted hypergraph model to obtain the salient object. The experimental results on three benchmark datasets demonstrate that the proposed method performs better than some other state-of-the-art methods.

scholarly journals SuperVAE: Superpixelwise Variational Autoencoder for Salient Object Detection

2019 ◽  
Vol 33 ◽  
pp. 8569-8576 ◽  
Author(s):  
Bo Li ◽  
Zhengxing Sun ◽  
Yuqi Guo
Keyword(s):  
Deep Learning ◽  
Object Detection ◽  
Saliency Detection ◽  
Salient Object Detection ◽  
Salient Object ◽  
Image Saliency ◽  
Spatial Consistency ◽  
Variational Autoencoder ◽  
Benchmark Datasets ◽  
Supervised Methods

Image saliency detection has recently witnessed rapid progress due to deep neural networks. However, there still exist many important problems in the existing deep learning based methods. Pixel-wise convolutional neural network (CNN) methods suffer from blurry boundaries due to the convolutional and pooling operations. While region-based deep learning methods lack spatial consistency since they deal with each region independently. In this paper, we propose a novel salient object detection framework using a superpixelwise variational autoencoder (SuperVAE) network. We first use VAE to model the image background and then separate salient objects from the background through the reconstruction residuals. To better capture semantic and spatial contexts information, we also propose a perceptual loss to take advantage from deep pre-trained CNNs to train our SuperVAE network. Without the supervision of mask-level annotated data, our method generates high quality saliency results which can better preserve object boundaries and maintain the spatial consistency. Extensive experiments on five wildly-used benchmark datasets show that the proposed method achieves superior or competitive performance compared to other algorithms including the very recent state-of-the-art supervised methods.

scholarly journals Salient Object Detection Based on Background Feature Clustering

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Kan Huang ◽  
Yong Zhang ◽  
Bo Lv ◽  
Yongbiao Shi
Keyword(s):  
Object Detection ◽  
Clustering Algorithm ◽  
Geodesic Distance ◽  
Saliency Map ◽  
Salient Object Detection ◽  
Salient Object ◽  
Edge Preserving ◽  
Feature Clustering ◽  
Background Distribution ◽  
Extensive Evaluation

Automatic estimation of salient object without any prior knowledge tends to greatly enhance many computer vision tasks. This paper proposes a novel bottom-up based framework for salient object detection by first modeling background and then separating salient objects from background. We model the background distribution based on feature clustering algorithm, which allows for fully exploiting statistical and structural information of the background. Then a coarse saliency map is generated according to the background distribution. To be more discriminative, the coarse saliency map is enhanced by a two-step refinement which is composed of edge-preserving element-level filtering and upsampling based on geodesic distance. We provide an extensive evaluation and show that our proposed method performs favorably against other outstanding methods on two most commonly used datasets. Most importantly, the proposed approach is demonstrated to be more effective in highlighting the salient object uniformly and robust to background noise.

Salient object detection based on novel graph model

2019 ◽  
Vol 65 ◽  
pp. 102676 ◽  
Author(s):  
Yu Pang ◽  
Xiaosheng Yu ◽  
Ying Wang ◽  
Chengdong Wu
Keyword(s):  
Object Detection ◽  
Graph Model ◽  
Salient Object Detection ◽  
Salient Object

scholarly journals RGB-D salient object detection: A survey

2021 ◽  
Author(s):  
Tao Zhou ◽  
Deng-Ping Fan ◽  
Ming-Ming Cheng ◽  
Jianbing Shen ◽  
Ling Shao
Keyword(s):  
Object Detection ◽  
Spatial Information ◽  
Salient Object Detection ◽  
Future Research ◽  
Salient Object ◽  
Human Visual Perception ◽  
Depth Sensors ◽  
Depth Maps ◽  
Benchmark Datasets ◽  
Comprehensive Survey

AbstractSalient object detection, which simulates human visual perception in locating the most significant object(s) in a scene, has been widely applied to various computer vision tasks. Now, the advent of depth sensors means that depth maps can easily be captured; this additional spatial information can boost the performance of salient object detection. Although various RGB-D based salient object detection models with promising performance have been proposed over the past several years, an in-depth understanding of these models and the challenges in this field remains lacking. In this paper, we provide a comprehensive survey of RGB-D based salient object detection models from various perspectives, and review related benchmark datasets in detail. Further, as light fields can also provide depth maps, we review salient object detection models and popular benchmark datasets from this domain too. Moreover, to investigate the ability of existing models to detect salient objects, we have carried out a comprehensive attribute-based evaluation of several representative RGB-D based salient object detection models. Finally, we discuss several challenges and open directions of RGB-D based salient object detection for future research. All collected models, benchmark datasets, datasets constructed for attribute-based evaluation, and related code are publicly available at https://github.com/taozh2017/RGBD-SODsurvey.

scholarly journals F³Net: Fusion, Feedback and Focus for Salient Object Detection

2020 ◽  
Vol 34 (07) ◽  
pp. 12321-12328 ◽  
Author(s):  
Jun Wei ◽  
Shuhui Wang ◽  
Qingming Huang
Keyword(s):  
Object Detection ◽  
Feature Fusion ◽  
Receptive Fields ◽  
Feedback Mechanism ◽  
Salient Object Detection ◽  
Salient Object ◽  
Structure Information ◽  
Multi Stage ◽  
Benchmark Datasets ◽  
Multi Level

Most of existing salient object detection models have achieved great progress by aggregating multi-level features extracted from convolutional neural networks. However, because of the different receptive fields of different convolutional layers, there exists big differences between features generated by these layers. Common feature fusion strategies (addition or concatenation) ignore these differences and may cause suboptimal solutions. In this paper, we propose the F3Net to solve above problem, which mainly consists of cross feature module (CFM) and cascaded feedback decoder (CFD) trained by minimizing a new pixel position aware loss (PPA). Specifically, CFM aims to selectively aggregate multi-level features. Different from addition and concatenation, CFM adaptively selects complementary components from input features before fusion, which can effectively avoid introducing too much redundant information that may destroy the original features. Besides, CFD adopts a multi-stage feedback mechanism, where features closed to supervision will be introduced to the output of previous layers to supplement them and eliminate the differences between features. These refined features will go through multiple similar iterations before generating the final saliency maps. Furthermore, different from binary cross entropy, the proposed PPA loss doesn't treat pixels equally, which can synthesize the local structure information of a pixel to guide the network to focus more on local details. Hard pixels from boundaries or error-prone parts will be given more attention to emphasize their importance. F3Net is able to segment salient object regions accurately and provide clear local details. Comprehensive experiments on five benchmark datasets demonstrate that F3Net outperforms state-of-the-art approaches on six evaluation metrics. Code will be released at https://github.com/weijun88/F3Net.

scholarly journals Progressive Feature Polishing Network for Salient Object Detection

2020 ◽  
Vol 34 (07) ◽  
pp. 12128-12135 ◽  
Author(s):  
Bo Wang ◽  
Quan Chen ◽  
Min Zhou ◽  
Zhiqiang Zhang ◽  
Xiaogang Jin ◽  
...  
Keyword(s):  
Object Detection ◽  
State Of The Art ◽  
Hierarchical Structures ◽  
Salient Object Detection ◽  
Salient Object ◽  
Post Processing ◽  
Feature Maps ◽  
Multiple Feature ◽  
Benchmark Datasets ◽  
Multi Level

Feature matters for salient object detection. Existing methods mainly focus on designing a sophisticated structure to incorporate multi-level features and filter out cluttered features. We present Progressive Feature Polishing Network (PFPN), a simple yet effective framework to progressively polish the multi-level features to be more accurate and representative. By employing multiple Feature Polishing Modules (FPMs) in a recurrent manner, our approach is able to detect salient objects with fine details without any post-processing. A FPM parallelly updates the features of each level by directly incorporating all higher level context information. Moreover, it can keep the dimensions and hierarchical structures of the feature maps, which makes it flexible to be integrated with any CNN-based models. Empirical experiments show that our results are monotonically getting better with increasing number of FPMs. Without bells and whistles, PFPN outperforms the state-of-the-art methods significantly on five benchmark datasets under various evaluation metrics. Our code is available at: https://github.com/chenquan-cq/PFPN.

Graph model-based salient object detection using objectness and multiple saliency cues

2019 ◽  
Vol 323 ◽  
pp. 188-202 ◽  
Author(s):  
Yuzhu Ji ◽  
Haijun Zhang ◽  
Kuo-Kun Tseng ◽  
Tommy W.S. Chow ◽  
Q. M. Jonathan Wu
Keyword(s):  
Object Detection ◽  
Graph Model ◽  
Salient Object Detection ◽  
Salient Object ◽  
Model Based

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.

Sign in / Sign up

Export Citation Format

Share Document