scholarly journals Appearance-Based Salient Regions Detection Using Side-Specific Dictionaries

Sensors ◽  
2019 ◽  
Vol 19 (2) ◽  
pp. 421 ◽  
Author(s):  
Mian Fareed ◽  
Qi Chun ◽  
Gulnaz Ahmed ◽  
Adil Murtaza ◽  
Muhammad Asif ◽  
...  
Keyword(s):  
Visual Information ◽  
Saliency Detection ◽  
Saliency Map ◽  
Salient Region Detection ◽  
Smart Systems ◽  
Salient Region ◽  
Region Detection ◽  
Saliency Maps ◽  
Image Saliency ◽  
High Level

Image saliency detection is a very helpful step in many computer vision-based smart systems to reduce the computational complexity by only focusing on the salient parts of the image. Currently, the image saliency is detected through representation-based generative schemes, as these schemes are helpful for extracting the concise representations of the stimuli and to capture the high-level semantics in visual information with a small number of active coefficients. In this paper, we propose a novel framework for salient region detection that uses appearance-based and regression-based schemes. The framework segments the image and forms reconstructive dictionaries from four sides of the image. These side-specific dictionaries are further utilized to obtain the saliency maps of the sides. A unified version of these maps is subsequently employed by a representation-based model to obtain a contrast-based salient region map. The map is used to obtain two regression-based maps with LAB and RGB color features that are unified through the optimization-based method to achieve the final saliency map. Furthermore, the side-specific reconstructive dictionaries are extracted from the boundary and the background pixels, which are enriched with geometrical and visual information. The approach has been thoroughly evaluated on five datasets and compared with the seven most recent approaches. The simulation results reveal that our model performs favorably in comparison with the current saliency detection schemes.

scholarly journals Salient Region Detection Using Diffusion Process with Nonlocal Connections

2018 ◽  
Vol 8 (12) ◽  
pp. 2526 ◽  
Author(s):  
Huiyuan Luo ◽  
Guangliang Han ◽  
Peixun Liu ◽  
Yanfeng Wu
Keyword(s):  
Saliency Detection ◽  
Gaussian Model ◽  
Single Layer ◽  
Saliency Map ◽  
Detection Methods ◽  
Sparse Graph ◽  
Salient Region Detection ◽  
Salient Region ◽  
Region Detection ◽  
Benchmark Datasets

Diffusion-based salient region detection methods have gained great popularity. In most diffusion-based methods, the saliency values are ranked on 2-layer neighborhood graph by connecting each node to its neighboring nodes and the nodes sharing common boundaries with its neighboring nodes. However, only considering the local relevance between neighbors, the salient region may be heterogeneous and even wrongly suppressed, especially when the features of salient object are diverse. In order to address the issue, we present an effective saliency detection method using diffusing process on the graph with nonlocal connections. First, a saliency-biased Gaussian model is used to refine the saliency map based on the compactness cue, and then, the saliency information of compactness is diffused on 2-layer sparse graph with nonlocal connections. Second, we obtain the contrast of each superpixel by restricting the reference region to the background. Similarly, a saliency-biased Gaussian refinement model is generated and the saliency information based on the uniqueness cue is propagated on the 2-layer sparse graph. We linearly integrate the initial saliency maps based on the compactness and uniqueness cues due to the complementarity to each other. Finally, to obtain a highlighted and homogeneous saliency map, a single-layer updating and multi-layer integrating scheme is presented. Comprehensive experiments on four benchmark datasets demonstrate that the proposed method performs better in terms of various evaluation metrics.

scholarly journals From Pixels to Region: A Salient Region Detection Algorithm for Location-Quantification Image

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Mengmeng Zhang ◽  
Zhi Liu ◽  
Huan Zhou ◽  
Jian Wang
Keyword(s):  
Saliency Detection ◽  
Detection Algorithm ◽  
Color Distribution ◽  
Salient Region Detection ◽  
Salient Region ◽  
Region Detection ◽  
Full Resolution ◽  
Image Saliency ◽  
Image Pixels ◽  
Segmentation Image

Image saliency detection has become increasingly important with the development of intelligent identification and machine vision technology. This process is essential for many image processing algorithms such as image retrieval, image segmentation, image recognition, and adaptive image compression. We propose a salient region detection algorithm for full-resolution images. This algorithm analyzes the randomness and correlation of image pixels and pixel-to-region saliency computation mechanism. The algorithm first obtains points with more saliency probability by using the improved smallest univalue segment assimilating nucleus operator. It then reconstructs the entire saliency region detection by taking these points as reference and combining them with image spatial color distribution, as well as regional and global contrasts. The results for subjective and objective image saliency detection show that the proposed algorithm exhibits outstanding performance in terms of technology indices such as precision and recall rates.

Multi-Resolution Salient Region Detection in Frequency Domain

2014 ◽  
Vol 6 (1) ◽  
pp. 60-70
Author(s):  
Yingchun Guo ◽  
Yanhong Feng ◽  
Gang Yan ◽  
Shuo Shi
Keyword(s):  
Frequency Domain ◽  
Euclidean Distance ◽  
Detection Method ◽  
Saliency Map ◽  
Normal Distribution Function ◽  
Salient Region Detection ◽  
Salient Region ◽  
Region Detection ◽  
Proposed Model ◽  
Salient Regions

Salient region detection is a challenge problem in computer vision, which is useful in image segmentation, region-based image retrieval, and so on. In this paper we present a multi-resolution salient region detection method in frequency domain which can highlight salient regions with well-defined boundaries of object. The original image is sub-sampled into three multi-resolution layers, and for each layer the luminance and color salient features are extracted in frequency domain. Then, the significant values are calculated by using invariant laws of Euclidean distance in Lab space and the normal distribution function is used to specify the salient map in each layer in order to remove noise and enhance the correlation among the vicinity pixels. The final saliency map is obtained by normalizing and merging the multi-resolution salient maps. Experimental evaluation depicts the promising results from the proposed model by outperforming the state-of-art frequency-tuned model.

scholarly journals Salient Region Detection by Fusing Foreground and Background Cues Extracted from Single Image

2016 ◽  
Vol 2016 ◽  
pp. 1-18 ◽  
Author(s):  
Qiangqiang Zhou ◽  
Weidong Zhao ◽  
Lin Zhang ◽  
Zhicheng Wang
Keyword(s):  
Saliency Detection ◽  
Computational Cost ◽  
Detection Algorithm ◽  
Saliency Map ◽  
Local Contrast ◽  
Feature Maps ◽  
Color Distribution ◽  
Salient Region Detection ◽  
Region Detection ◽  
Application Fields

Saliency detection is an important preprocessing step in many application fields such as computer vision, robotics, and graphics to reduce computational cost by focusing on significant positions and neglecting the nonsignificant in the scene. Different from most previous methods which mainly utilize the contrast of low-level features, various feature maps are fused in a simple linear weighting form. In this paper, we propose a novel salient object detection algorithm which takes both background and foreground cues into consideration and integrate a bottom-up coarse salient regions extraction and a top-down background measure via boundary labels propagation into a unified optimization framework to acquire a refined saliency detection result. Wherein the coarse saliency map is also fused by three components, the first is local contrast map which is in more accordance with the psychological law, the second is global frequency prior map, and the third is global color distribution map. During the formation of background map, first we construct an affinity matrix and select some nodes which lie on border as labels to represent the background and then carry out a propagation to generate the regional background map. The evaluation of the proposed model has been implemented on four datasets. As demonstrated in the experiments, our proposed method outperforms most existing saliency detection models with a robust performance.

scholarly journals Research on an Infrared Multi-Target Saliency Detection Algorithm under Sky Background Conditions

Sensors ◽  
2020 ◽  
Vol 20 (2) ◽  
pp. 459
Author(s):  
Shaosheng Dai ◽  
Dongyang Li
Keyword(s):  
Saliency Detection ◽  
Infrared Image ◽  
Detection Algorithm ◽  
Saliency Map ◽  
Phase Spectrum ◽  
Background Suppression ◽  
Suppression Effect ◽  
Multi Scale ◽  
Saliency Maps ◽  
Image Saliency

Aiming at solving the problem of incomplete saliency detection and unclear boundaries in infrared multi-target images with different target sizes and low signal-to-noise ratio under sky background conditions, this paper proposes a saliency detection method for multiple targets based on multi-saliency detection. The multiple target areas of the infrared image are mainly bright and the background areas are dark. Combining with the multi-scale top hat (Top-hat) transformation, the image is firstly corroded and expanded to extract the subtraction of light and shade parts and reconstruct the image to reduce the interference of sky blurred background noise. Then the image obtained by a multi-scale Top-hat transformation is transformed from the time domain to the frequency domain, and the spectral residuals and phase spectrum are extracted directly to obtain two kinds of image saliency maps by multi-scale Gauss filtering reconstruction, respectively. On the other hand, the quaternion features are extracted directly to transform the phase spectrum, and then the phase spectrum is reconstructed to obtain one kind of image saliency map by the Gauss filtering. Finally, the above three saliency maps are fused to complete the saliency detection of infrared images. The test results show that after the experimental analysis of infrared video photographs and the comparative analysis of Receiver Operating Characteristic (ROC) curve and Area Under the Curve (AUC) index, the infrared image saliency map generated by this method has clear target details and good background suppression effect, and the AUC index performance is good, reaching over 99%. It effectively improves the multi-target saliency detection effect of the infrared image under the sky background and is beneficial to subsequent detection and tracking of image targets.

Salient Region Detection for Biometric Watermarking

2011 ◽  
pp. 218-236
Author(s):  
Ma Bin ◽  
Li Chun-lei ◽  
Wang Yun-hong ◽  
Bai Xiao
Keyword(s):  
Vision System ◽  
Region Of Interest ◽  
Visual Saliency ◽  
Saliency Map ◽  
Human Vision ◽  
Identification Accuracy ◽  
Biometric Data ◽  
Salient Region Detection ◽  
Salient Region ◽  
Region Detection

Visual saliency, namely the perceptual significance to human vision system (HVS), is a quality that differentiates an object from its neighbors. Detection of salient regions which contain prominent features and represent main contents of the visual scene, has obtained wide utilization among computer vision based applications, such as object tracking and classification, region-of-interest (ROI) based image compression, etc. Specially, as for biometric authentication system, whose objective is to distinguish the identification of people through biometric data (e.g. fingerprint, iris, face etc.), the most important metric is distinguishability. Consequently, in biometric watermarking fields, there has been a great need of good metrics for feature prominency. In this chapter, we present two salient-region-detection based biometric watermarking scenarios, in which robust annotation and fragile authentication watermark are respectively applied to biometric systems. Saliency map plays an important role of perceptual mask that adaptively select watermarking strength and position, therefore controls the distortion introduced by watermark and preserves the identification accuracy of biometric images.

Salient Region Detection for Biometric Watermarking

2013 ◽  
pp. 201-219
Author(s):  
Bin Ma ◽  
Chun-lei Li ◽  
Yun-hong Wang ◽  
Xiao Bai
Keyword(s):  
Vision System ◽  
Region Of Interest ◽  
Visual Saliency ◽  
Saliency Map ◽  
Human Vision ◽  
Identification Accuracy ◽  
Biometric Data ◽  
Salient Region Detection ◽  
Salient Region ◽  
Region Detection

Visual saliency, namely the perceptual significance to human vision system (HVS), is a quality that differentiates an object from its neighbors. Detection of salient regions which contain prominent features and represent main contents of the visual scene, has obtained wide utilization among computer vision based applications, such as object tracking and classification, region-of-interest (ROI) based image compression, etc. Specially, as for biometric authentication system, whose objective is to distinguish the identification of people through biometric data (e.g. fingerprint, iris, face etc.), the most important metric is distinguishability. Consequently, in biometric watermarking fields, there has been a great need of good metrics for feature prominency. In this chapter, we present two salient-region-detection based biometric watermarking scenarios, in which robust annotation and fragile authentication watermark are respectively applied to biometric systems. Saliency map plays an important role of perceptual mask that adaptively select watermarking strength and position, therefore controls the distortion introduced by watermark and preserves the identification accuracy of biometric images.

scholarly journals Saliency Aggregation: Multifeature and Neighbor Based Salient Region Detection for Social Images

2018 ◽  
Vol 2018 ◽  
pp. 1-16
Author(s):  
Ye Liang ◽  
Congyan Lang ◽  
Jian Yu ◽  
Hongzhe Liu ◽  
Nan Ma
Keyword(s):  
Saliency Detection ◽  
Visual Saliency ◽  
Salient Region Detection ◽  
Salient Region ◽  
Region Detection ◽  
Image Appearance ◽  
Deep Convolution Neural Network ◽  
Salient Regions ◽  
Social Images ◽  
Visual Saliency Models

The popularity of social networks has brought the rapid growth of social images which have become an increasingly important image type. One of the most obvious attributes of social images is the tag. However, the sate-of-the-art methods fail to fully exploit the tag information for saliency detection. Thus this paper focuses on salient region detection of social images using both image appearance features and image tag cues. First, a deep convolution neural network is built, which considers both appearance features and tag features. Second, tag neighbor and appearance neighbor based saliency aggregation terms are added to the saliency model to enhance salient regions. The aggregation method is dependent on individual images and considers the performance gaps appropriately. Finally, we also have constructed a new large dataset of challenging social images and pixel-wise saliency annotations to promote further researches and evaluations of visual saliency models. Extensive experiments show that the proposed method performs well on not only the new dataset but also several state-of-the-art saliency datasets.

scholarly journals Graph-Based Salient Region Detection through Linear Neighborhoods

2016 ◽  
Vol 2016 ◽  
pp. 1-11
Author(s):  
Lijuan Xu ◽  
Fan Wang ◽  
Yan Yang ◽  
Xiaopeng Hu ◽  
Yuanyuan Sun
Keyword(s):  
Weight Function ◽  
Linear Combination ◽  
Detection Method ◽  
Saliency Detection ◽  
Detection Methods ◽  
Salient Region Detection ◽  
Salient Region ◽  
Region Detection ◽  
Graph Labelling ◽  
Gaussian Weight

Pairwise neighboring relationships estimated by Gaussian weight function have been extensively adopted in the graph-based salient region detection methods recently. However, the learning of the parameters remains a problem as nonoptimal models will affect the detection results significantly. To tackle this challenge, we first apply the adjacent information provided by all neighbors of each node to construct the undirected weight graph, based on the assumption that every node can be optimally reconstructed by a linear combination of its neighbors. Then, the saliency detection is modeled as the process of graph labelling by learning from partially selected seeds (labeled data) in the graph. The promising experimental results presented on some datasets demonstrate the effectiveness and reliability of our proposed graph-based saliency detection method through linear neighborhoods.

scholarly journals Military Object Real-Time Detection Technology Combined with Visual Salience and Psychology

Electronics ◽  
2018 ◽  
Vol 7 (10) ◽  
pp. 216 ◽  
Author(s):  
Xia Hua ◽  
Xinqing Wang ◽  
Dong Wang ◽  
Jie Huang ◽  
Xiaodong Hu
Keyword(s):  
Visual Information ◽  
Feature Fusion ◽  
Visual Salience ◽  
Salient Region Detection ◽  
Salient Region ◽  
Detection Model ◽  
Region Detection ◽  
Accurate Detection ◽  
Detection Technology ◽  
Salient Regions

This paper presents a method of military object detection through the combination of human visual salience and visual psychology, so as to achieve rapid and accurate detection of military objects on the vast and complex battlefield. Inspired by the process of human visual information processing, this paper establishes a salient region detection model based on double channel and feature fusion. In this model the pre-attention channel is to process information on the position and contrast of images, and the sub-attention channel is to integrate information on primary visual features first and then merges results of the two channels to determine the salient region. The main theory of Gestalt visual psychology is then used as the constraint condition to integrate the candidate salient regions and to obtain the object figure with overall perception. After that, the efficient sub-window search method is used to detect and filter the object in order to determine the location and range of objects. The experimental results show that, when compared with the existing algorithms, the algorithm proposed in this paper has prominent advantages in precision, effectiveness, and simplicity, which not only significantly reduces the effectiveness of battlefield camouflage and deception but also achieves the rapid and accurate detection of military objects, thus promoting its application prospect.

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