Co-Saliency Detection via Co-Salient Object Discovery and Recovery

2015 ◽  
Vol 22 (11) ◽  
pp. 2073-2077 ◽  
Author(s):  
Linwei Ye ◽  
Zhi Liu ◽  
Junhao Li ◽  
Wan-Lei Zhao ◽  
Liquan Shen
Keyword(s):  
Saliency Detection ◽  
Salient Object ◽  
Object Discovery

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 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.

Double Feature Combination: Region Contrast for Visual Salient Object Detection

2012 ◽  
Vol 239-240 ◽  
pp. 811-815
Author(s):  
Zhi Hai Sun ◽  
Teng Song ◽  
Wen Hui Zhou ◽  
Hua Zhang
Keyword(s):  
Object Detection ◽  
Saliency Detection ◽  
Visual Saliency ◽  
Recall Rate ◽  
Detection Algorithm ◽  
Salient Object Detection ◽  
Color Contrast ◽  
Salient Object ◽  
Histograms Of Oriented Gradients ◽  
Visual Saliency Detection

Visual saliency detection has become an important step between computer vision and digital image processing. Recent methods almost form a computational model based on color, which are difficult to overcome the shortcoming with cluttered and textured background. This paper proposes a novel salient object detection algorithm integrating with region color contrast and histograms of oriented gradients (HoG). Extensively experiments show that our algorithm outperforms other state-of-art saliency methods, yielding higher precision and better recall rate, even lower mean absolution error.

scholarly journals Weakly-Supervised Saliency Detection via Salient Object Subitizing

2021 ◽  
pp. 1-1
Author(s):  
Xiaoyang Zheng ◽  
Xin Tan ◽  
Jie Zhou ◽  
Lizhuang Ma ◽  
Rynson W.H. Lau
Keyword(s):  
Saliency Detection ◽  
Salient Object ◽  
Weakly Supervised

Unseen Salient Object Discovery for Monocular Robot Vision

2020 ◽  
Vol 5 (2) ◽  
pp. 1484-1491 ◽  
Author(s):  
Darren M. Chan ◽  
Laurel D. Riek
Keyword(s):  
Robot Vision ◽  
Salient Object ◽  
Object Discovery

scholarly journals Visual Saliency Detection Using a Rule-Based Aggregation Approach

2019 ◽  
Vol 9 (10) ◽  
pp. 2015 ◽  
Author(s):  
Alberto Lopez-Alanis ◽  
Rocio A. Lizarraga-Morales ◽  
Raul E. Sanchez-Yanez ◽  
Diana E. Martinez-Rodriguez ◽  
Marco A. Contreras-Cruz
Keyword(s):  
State Of The Art ◽  
Saliency Detection ◽  
Visual Saliency ◽  
Threshold Value ◽  
Salient Object ◽  
Rule Based ◽  
Rule Based System ◽  
Training Stage ◽  
Processing Step ◽  
Visual Saliency Detection

In this paper, we propose an approach for salient pixel detection using a rule-based system. In our proposal, rules are automatically learned by combining four saliency models. The learned rules are utilized for the detection of pixels of the salient object in a visual scene. The proposed methodology consists of two main stages. Firstly, in the training stage, the knowledge extracted from outputs of four state-of-the-art saliency models is used to induce an ensemble of rough-set-based rules. Secondly, the induced rules are utilized by our system to determine, in a binary manner, the pixels corresponding to the salient object within a scene. Being independent of any threshold value, such a method eliminates any midway uncertainty and exempts us from performing a post-processing step as is required in most approaches to saliency detection. The experimental results on three datasets show that our method obtains stable and better results than state-of-the-art models. Moreover, it can be used as a pre-processing stage in computer vision-based applications in diverse areas such as robotics, image segmentation, marketing, and image compression.

scholarly journals Detecting Salient Image Objects Using Color Histogram Clustering for Region Granularity

2021 ◽  
Vol 7 (9) ◽  
pp. 187
Author(s):  
Seena Joseph ◽  
Oludayo O. Olugbara
Keyword(s):  
Object Detection ◽  
Saliency Detection ◽  
Contrast Ratio ◽  
Salient Object Detection ◽  
Color Images ◽  
Color Histogram ◽  
Detection Methods ◽  
Color Contrast ◽  
Salient Object ◽  
Selection Of

Salient object detection represents a novel preprocessing stage of many practical image applications in the discipline of computer vision. Saliency detection is generally a complex process to copycat the human vision system in the processing of color images. It is a convoluted process because of the existence of countless properties inherent in color images that can hamper performance. Due to diversified color image properties, a method that is appropriate for one category of images may not necessarily be suitable for others. The selection of image abstraction is a decisive preprocessing step in saliency computation and region-based image abstraction has become popular because of its computational efficiency and robustness. However, the performances of the existing region-based salient object detection methods are extremely hooked on the selection of an optimal region granularity. The incorrect selection of region granularity is potentially prone to under- or over-segmentation of color images, which can lead to a non-uniform highlighting of salient objects. In this study, the method of color histogram clustering was utilized to automatically determine suitable homogenous regions in an image. Region saliency score was computed as a function of color contrast, contrast ratio, spatial feature, and center prior. Morphological operations were ultimately performed to eliminate the undesirable artifacts that may be present at the saliency detection stage. Thus, we have introduced a novel, simple, robust, and computationally efficient color histogram clustering method that agglutinates color contrast, contrast ratio, spatial feature, and center prior for detecting salient objects in color images. Experimental validation with different categories of images selected from eight benchmarked corpora has indicated that the proposed method outperforms 30 bottom-up non-deep learning and seven top-down deep learning salient object detection methods based on the standard performance metrics.

scholarly journals Salient Object Detection by Lossless Feature Reflection

2018 ◽  
Author(s):  
Pingping Zhang ◽  
Wei Liu ◽  
Huchuan Lu ◽  
Chunhua Shen
Keyword(s):  
Object Detection ◽  
Large Scale ◽  
Structural Information ◽  
Saliency Detection ◽  
Feature Learning ◽  
Salient Object Detection ◽  
Superior Performance ◽  
Salient Object ◽  
Convolutional Network ◽  
Performance Improvements

Salient object detection, which aims to identify and locate the most salient pixels or regions in images, has been attracting more and more interest due to its various real-world applications. However, this vision task is quite challenging, especially under complex image scenes. Inspired by the intrinsic reflection of natural images, in this paper we propose a novel feature learning framework for large-scale salient object detection. Specifically, we design a symmetrical fully convolutional network (SFCN) to learn complementary saliency features under the guidance of lossless feature reflection. The location information, together with contextual and semantic information, of salient objects are jointly utilized to supervise the proposed network for more accurate saliency predictions. In addition, to overcome the blurry boundary problem, we propose a new structural loss function to learn clear object boundaries and spatially consistent saliency. The coarse prediction results are effectively refined by these structural information for performance improvements. Extensive experiments on seven saliency detection datasets demonstrate that our approach achieves consistently superior performance and outperforms the very recent state-of-the-art methods.

Sign in / Sign up

Export Citation Format

Share Document