Hybrid Local and Global Deep-Learning Architecture for Salient-Object Detection

Salient-object detection is a fundamental and the most challenging problem in computer vision. This paper focuses on the detection of salient objects, especially in low-contrast images. To this end, a hybrid deep-learning architecture is proposed where features are extracted on both the local and global level. These features are then integrated to extract the exact boundary of the object of interest in an image. Experimentation was performed on five standard datasets, and results were compared with state-of-the-art approaches. Both qualitative and quantitative analyses showed the robustness of the proposed architecture.

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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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Overview of deep-learning based methods for salient object detection in videos

Pattern Recognition ◽

10.1016/j.patcog.2020.107340 ◽

2020 ◽

Vol 104 ◽

pp. 107340

Author(s):

Qiong Wang ◽

Lu Zhang ◽

Yan Li ◽

Kidiyo Kpalma

Keyword(s):

Deep Learning ◽

Object Detection ◽

Salient Object Detection ◽

Salient Object

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SuperVAE: Superpixelwise Variational Autoencoder for Salient Object Detection

Proceedings of the AAAI Conference on Artificial Intelligence ◽

10.1609/aaai.v33i01.33018569 ◽

2019 ◽

Vol 33 ◽

pp. 8569-8576 ◽

Cited By ~ 2

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.

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RGBD Salient Object Detection using Spatially Coherent Deep Learning Framework

2018 IEEE 23rd International Conference on Digital Signal Processing (DSP) ◽

10.1109/icdsp.2018.8631584 ◽

2018 ◽

Cited By ~ 4

Author(s):

Posheng Huang ◽

Chin-Han Shen ◽

Hsu-Feng Hsiao

Keyword(s):

Deep Learning ◽

Object Detection ◽

Salient Object Detection ◽

Salient Object ◽

Learning Framework

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Salient Object Detection Techniques in Computer Vision—A Survey

Entropy ◽

10.3390/e22101174 ◽

2020 ◽

Vol 22 (10) ◽

pp. 1174

Author(s):

Ashish Kumar Gupta ◽

Ayan Seal ◽

Mukesh Prasad ◽

Pritee Khanna

Keyword(s):

Computer Vision ◽

Deep Learning ◽

Object Detection ◽

Large Scale ◽

Research Work ◽

Salient Object Detection ◽

Future Research ◽

Automatic Identification ◽

Salient Object ◽

Detection Techniques

Detection and localization of regions of images that attract immediate human visual attention is currently an intensive area of research in computer vision. The capability of automatic identification and segmentation of such salient image regions has immediate consequences for applications in the field of computer vision, computer graphics, and multimedia. A large number of salient object detection (SOD) methods have been devised to effectively mimic the capability of the human visual system to detect the salient regions in images. These methods can be broadly categorized into two categories based on their feature engineering mechanism: conventional or deep learning-based. In this survey, most of the influential advances in image-based SOD from both conventional as well as deep learning-based categories have been reviewed in detail. Relevant saliency modeling trends with key issues, core techniques, and the scope for future research work have been discussed in the context of difficulties often faced in salient object detection. Results are presented for various challenging cases for some large-scale public datasets. Different metrics considered for assessment of the performance of state-of-the-art salient object detection models are also covered. Some future directions for SOD are presented towards end.

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Revise-Net: Exploiting Reverse Attention Mechanism for Salient Object Detection

Remote Sensing ◽

10.3390/rs13234941 ◽

2021 ◽

Vol 13 (23) ◽

pp. 4941

Author(s):

Rukhshanda Hussain ◽

Yash Karbhari ◽

Muhammad Fazal Ijaz ◽

Marcin Woźniak ◽

Pawan Kumar Singh ◽

...

Keyword(s):

Object Detection ◽

State Of The Art ◽

Similarity Index ◽

Saliency Map ◽

Salient Object Detection ◽

Semantic Features ◽

Salient Object ◽

Fully Convolutional Neural Networks ◽

Boundary Estimation ◽

Prediction Module

Recently, deep learning-based methods, especially utilizing fully convolutional neural networks, have shown extraordinary performance in salient object detection. Despite its success, the clean boundary detection of the saliency objects is still a challenging task. Most of the contemporary methods focus on exclusive edge detection modules in order to avoid noisy boundaries. In this work, we propose leveraging on the extraction of finer semantic features from multiple encoding layers and attentively re-utilize it in the generation of the final segmentation result. The proposed Revise-Net model is divided into three parts: (a) the prediction module, (b) a residual enhancement module, and (c) reverse attention modules. Firstly, we generate the coarse saliency map through the prediction modules, which are fine-tuned in the enhancement module. Finally, multiple reverse attention modules at varying scales are cascaded between the two networks to guide the prediction module by employing the intermediate segmentation maps generated at each downsampling level of the REM. Our method efficiently classifies the boundary pixels using a combination of binary cross-entropy, similarity index, and intersection over union losses at the pixel, patch, and map levels, thereby effectively segmenting the saliency objects in an image. In comparison with several state-of-the-art frameworks, our proposed Revise-Net model outperforms them with a significant margin on three publicly available datasets, DUTS-TE, ECSSD, and HKU-IS, both on regional and boundary estimation measures.

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Salient Object Detection with Semantic Priors

Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence ◽

10.24963/ijcai.2017/628 ◽

2017 ◽

Cited By ~ 5

Author(s):

Tam V. Nguyen ◽

Luoqi Liu

Keyword(s):

Object Detection ◽

State Of The Art ◽

Semantic Segmentation ◽

Saliency Map ◽

Salient Object Detection ◽

Salient Object ◽

Artificial Intelligence Research ◽

Semantic Map ◽

Regional Features ◽

Computational Sciences

Salient object detection has increasingly become a popular topic in cognitive and computational sciences, including computer vision and artificial intelligence research. In this paper, we propose integrating semantic priors into the salient object detection process. Our algorithm consists of three basic steps. Firstly, the explicit saliency map is obtained based on the semantic segmentation refined by the explicit saliency priors learned from the data. Next, the implicit saliency map is computed based on a trained model which maps the implicit saliency priors embedded into regional features with the saliency values. Finally, the explicit semantic map and the implicit map are adaptively fused to form a pixel-accurate saliency map which uniformly covers the objects of interest. We further evaluate the proposed framework on two challenging datasets, namely, ECSSD and HKUIS. The extensive experimental results demonstrate that our method outperforms other state-of-the-art methods.

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