Global and local information aggregation network for edge-aware salient object detection

Salient object detection (SOD) is a fundamental task in computer vision, which attempts to mimic human visual systems that rapidly respond to visual stimuli and locate visually salient objects in various scenes. Perceptual studies have revealed that visual contrast is the most important factor in bottom-up visual attention process. Many of the proposed models predict saliency maps based on the computation of visual contrast between salient regions and backgrounds. In this paper, we design an end-to-end multi-scale global contrast convolutional neural network (CNN) that explicitly learns hierarchical contrast information among global and local features of an image to infer its salient object regions. In contrast to many previous CNN based saliency methods that apply super-pixel segmentation to obtain homogeneous regions and then extract their CNN features before producing saliency maps region-wise, our network is pre-processing free without any additional stages, yet it predicts accurate pixel-wise saliency maps. Extensive experiments demonstrate that the proposed network generates high quality saliency maps that are comparable or even superior to those of state-of-the-art salient object detection architectures.

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Dual-Branch Feature Fusion Network for Salient Object Detection

Photonics ◽

10.3390/photonics9010044 ◽

2022 ◽

Vol 9 (1) ◽

pp. 44

Author(s):

Zhehan Song ◽

Zhihai Xu ◽

Jing Wang ◽

Huajun Feng ◽

Qi Li

Keyword(s):

Object Detection ◽

Feature Fusion ◽

Local Information ◽

Salient Object Detection ◽

Large Field ◽

Wide Field ◽

Global Information ◽

Layer By Layer ◽

Salient Object ◽

Information Layer

Proper features matter 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 the dual-branch feature fusion network (DBFFNet), a simple effective framework mainly composed of three modules: global information perception module, local information concatenation module and refinement fusion module. The local information of a salient object is extracted from the local information concatenation module. The global information perception module exploits the U-Net structure to transmit the global information layer by layer. By employing the refinement fusion module, our approach is able to refine features from two branches and detect salient objects with final details without any post-processing. Experiments on standard benchmarks demonstrate that our method outperforms almost all of the state-of-the-art methods in terms of accuracy, and achieves the best performance in terms of speed under fair settings. Moreover, we design a wide-field optical system and combine with DBFFNet to achieve salient object detection with large field of view.

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WGI-Net: A weighted group integration network for RGB-D salient object detection

Computational Visual Media ◽

10.1007/s41095-020-0200-x ◽

2021 ◽

Author(s):

Yanliang Ge ◽

Cong Zhang ◽

Kang Wang ◽

Ziqi Liu ◽

Hongbo Bi

Keyword(s):

Object Detection ◽

Information Aggregation ◽

Salient Object Detection ◽

Supplementary Information ◽

Depth Information ◽

Salient Object ◽

Detection Depth ◽

Group Integration ◽

Feature Information ◽

Depth Feature

AbstractSalient object detection is used as a pre-process in many computer vision tasks (such as salient object segmentation, video salient object detection, etc.). When performing salient object detection, depth information can provide clues to the location of target objects, so effective fusion of RGB and depth feature information is important. In this paper, we propose a new feature information aggregation approach, weighted group integration (WGI), to effectively integrate RGB and depth feature information. We use a dual-branch structure to slice the input RGB image and depth map separately and then merge the results separately by concatenation. As grouped features may lose global information about the target object, we also make use of the idea of residual learning, taking the features captured by the original fusion method as supplementary information to ensure both accuracy and completeness of the fused information. Experiments on five datasets show that our model performs better than typical existing approaches for four evaluation metrics.

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Region Diversity Based Saliency Density Maximization for Salient Object Detection

IEICE Transactions on Fundamentals of Electronics Communications and Computer Sciences ◽

10.1587/transfun.e96.a.394 ◽

2013 ◽

Vol E96.A (1) ◽

pp. 394-397 ◽

Cited By ~ 1

Author(s):

Xin HE ◽

Huiyun JING ◽

Qi HAN ◽

Xiamu NIU

Keyword(s):

Object Detection ◽

Salient Object Detection ◽

Salient Object

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Saliency detection in deep learning era: trends of development

Information and Control Systems ◽

10.31799/1684-8853-2019-3-10-36 ◽

2019 ◽

pp. 10-36 ◽

Cited By ~ 2

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.

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