Image Saliency Detection Based on Spatial Distribution Statistics of Image Patch

2020 ◽  
Author(s):  
Chao Jia ◽  
Shuqing Lin ◽  
Fanshu Kong
Keyword(s):  
Spatial Distribution ◽  
Saliency Detection ◽  
Image Patch ◽  
Image Saliency

Image Saliency Detection Based on Graph and Multi-Feature Diffusion

2020 ◽  
Vol 57 (4) ◽  
pp. 041020
Author(s):  
张莹莹 Zhang Yingying ◽  
葛洪伟 Ge Hongwei
Keyword(s):  
Saliency Detection ◽  
Image Saliency

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.

Fast DCT-based image saliency detection

2012 ◽  
Author(s):  
Xiaoshan Yang ◽  
Jianbing Shen ◽  
Chao Liang ◽  
Yun Zhu
Keyword(s):  
Saliency Detection ◽  
Image Saliency

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.

53-4: Image Saliency Detection based Depth Adjustment for Stereoscopic Image

2016 ◽  
Vol 47 (1) ◽  
pp. 727-730
Author(s):  
Zhenping Xia ◽  
Cheng Cheng ◽  
Fuyuan Hu ◽  
Zhancheng Zhang
Keyword(s):  
Saliency Detection ◽  
Stereoscopic Image ◽  
Image Saliency

scholarly journals Meaning and Attentional Guidance in Scenes: A Review of the Meaning Map Approach

Vision ◽  
2019 ◽  
Vol 3 (2) ◽  
pp. 19 ◽  
Author(s):  
John M. Henderson ◽  
Taylor R. Hayes ◽  
Candace E. Peacock ◽  
Gwendolyn Rehrig
Keyword(s):  
Spatial Distribution ◽  
Aesthetic Judgment ◽  
Semantic Features ◽  
Attentional Guidance ◽  
Saliency Maps ◽  
Eye Fixations ◽  
Image Saliency ◽  
Scene Description ◽  
Traditional Image

Perception of a complex visual scene requires that important regions be prioritized and attentionally selected for processing. What is the basis for this selection? Although much research has focused on image salience as an important factor guiding attention, relatively little work has focused on semantic salience. To address this imbalance, we have recently developed a new method for measuring, representing, and evaluating the role of meaning in scenes. In this method, the spatial distribution of semantic features in a scene is represented as a meaning map. Meaning maps are generated from crowd-sourced responses given by naïve subjects who rate the meaningfulness of a large number of scene patches drawn from each scene. Meaning maps are coded in the same format as traditional image saliency maps, and therefore both types of maps can be directly evaluated against each other and against maps of the spatial distribution of attention derived from viewers’ eye fixations. In this review we describe our work focusing on comparing the influences of meaning and image salience on attentional guidance in real-world scenes across a variety of viewing tasks that we have investigated, including memorization, aesthetic judgment, scene description, and saliency search and judgment. Overall, we have found that both meaning and salience predict the spatial distribution of attention in a scene, but that when the correlation between meaning and salience is statistically controlled, only meaning uniquely accounts for variance in attention.

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