Built-up area detection based on a Bayesian saliency model

2017 ◽  
Vol 15 (03) ◽  
pp. 1750027 ◽  
Author(s):  
Qingjie Liu ◽  
Di Huang ◽  
Yunhong Wang ◽  
Hong Wei ◽  
Yuanyan Tang
Keyword(s):  
Urban Growth ◽  
Urban Areas ◽  
Prior Probability ◽  
Visual Saliency ◽  
Saliency Map ◽  
Multi Scale ◽  
Saliency Model ◽  
Edge Distribution ◽  
Land Use Monitoring ◽  
Growth Detection

Built-up area detection is very important for applications such as urban planning, urban growth detection and land use monitoring. In this paper, we address the problem of built-up area detection from the perspective of visual saliency computation. Generally, areas containing buildings attract more attentions than forests, lands and other backgrounds. This paper explores a Bayesian saliency model to automatically detect urban areas. Firstly, prior probability is computed by using fast multi-scale edge distribution. Then the likelihood is obtained by modeling the distributions of color and orientation. Built-up areas are further detected by segmenting the final saliency map using Graph Cut algorithm. Experimental results demonstrate that the proposed method can extract built-up area efficiently and accurately.

A Saliency Detection Method Based on Wavelet Transform and Simple Priors

2014 ◽  
Vol 602-605 ◽  
pp. 2238-2241
Author(s):  
Jian Kun Chen ◽  
Zhi Wei Kang
Keyword(s):  
Wavelet Transform ◽  
Detection Method ◽  
Saliency Detection ◽  
Visual Saliency ◽  
Feature Maps ◽  
Multi Scale ◽  
Saliency Model ◽  
Proposed Model ◽  
Visual Saliency Model ◽  
Better Than

In this paper, we present a new visual saliency model, which based on Wavelet Transform and simple Priors. Firstly, we create multi-scale feature maps to represent different features from edge to texture in wavelet transform. Then we modulate local saliency at a location and its global saliency, combine the local saliency and global saliency to generate a new saliency .Finally, the final saliency is generated by combining the new saliency and two simple priors (color prior an location prior). Experimental evaluation shows the proposed model can achieve state-of-the-art results and better than the other models on a public available benchmark dataset.

scholarly journals Learning-Based Visual Saliency Model for Detecting Diabetic Macular Edema in Retinal Image

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Xiaochun Zou ◽  
Xinbo Zhao ◽  
Yongjia Yang ◽  
Na Li
Keyword(s):  
Macular Edema ◽  
Diabetic Macular Edema ◽  
Retinal Image ◽  
Visual Saliency ◽  
Saliency Map ◽  
Support Vector ◽  
Salient Region Detection ◽  
Region Detection ◽  
Saliency Model ◽  
Visual Saliency Model

This paper brings forth a learning-based visual saliency model method for detecting diagnostic diabetic macular edema (DME) regions of interest (RoIs) in retinal image. The method introduces the cognitive process of visual selection of relevant regions that arises during an ophthalmologist’s image examination. To record the process, we collected eye-tracking data of 10 ophthalmologists on 100 images and used this database as training and testing examples. Based on analysis, two properties (Feature Property and Position Property) can be derived and combined by a simple intersection operation to obtain a saliency map. The Feature Property is implemented by support vector machine (SVM) technique using the diagnosis as supervisor; Position Property is implemented by statistical analysis of training samples. This technique is able to learn the preferences of ophthalmologist visual behavior while simultaneously considering feature uniqueness. The method was evaluated using three popular saliency model evaluation scores (AUC, EMD, and SS) and three quality measurements (classical sensitivity, specificity, and Youden’sJstatistic). The proposed method outperforms 8 state-of-the-art saliency models and 3 salient region detection approaches devised for natural images. Furthermore, our model successfully detects the DME RoIs in retinal image without sophisticated image processing such as region segmentation.

Ship detection based on the multi-scale visual saliency model

2020 ◽  
Vol 28 (6) ◽  
pp. 1395-1403
Author(s):  
赵浩光 ZHAO Hao-guang ◽  
王平 WANG Ping ◽  
董超 DONG Chao ◽  
尚洋 SHANG Yang
Keyword(s):  
Visual Saliency ◽  
Ship Detection ◽  
Multi Scale ◽  
Saliency Model ◽  
Visual Saliency Model

A novel image recognition approach using multiscale saliency model and GoogLeNet

2020 ◽  
Vol 2020 (10) ◽  
pp. 97-1-97-8
Author(s):  
Guoan Yang ◽  
Libo Jian ◽  
Zhengzhi Lu ◽  
Junjie Yang ◽  
Deyang Liu
Keyword(s):  
Image Recognition ◽  
Recognition Task ◽  
Visual Saliency ◽  
Image Database ◽  
Visual Selective Attention ◽  
Original Image ◽  
Multi Scale ◽  
Saliency Model ◽  
Salient Regions ◽  
Visual Saliency Model

It is very good to apply the saliency model in the visual selective attention mechanism to the preprocessing process of image recognition. However, the mechanism of visual perception is still unclear, so this visual saliency model is not ideal. To this end, this paper proposes a novel image recognition approach using multiscale saliency model and GoogLeNet. First, a multi-scale convolutional neural network was taken advantage of constructing multiscale salient maps, which could be used as filters. Second, an original image was combined with the salient maps to generate the filtered image, which highlighted the salient regions and suppressed the background in the image. Third, the image recognition task was implemented by adopting the classical GoogLeNet model. In this paper, many experiments were completed by comparing four commonly used evaluation indicators on the standard image database MSRA10K. The experimental results show that the recognition results of the test images based on the proposed method are superior to some stateof- the-art image recognition methods, and are also more approximate to the results of human eye observation.

scholarly journals A Bioinspired Visual Saliency Model

2019 ◽  
Vol 37 (3) ◽  
pp. 503-508
Author(s):  
Ke Zhang ◽  
Xinbo Zhao ◽  
Rong Mo
Keyword(s):  
Visual Cortex ◽  
Primary Visual Cortex ◽  
Visual Saliency ◽  
Saliency Map ◽  
Input Image ◽  
Visual Signals ◽  
Bottom Up ◽  
Saliency Model ◽  
Visual Saliency Model ◽  
Public Datasets

This paper presents a bioinspired visual saliency model. The end-stopping mechanism in the primary visual cortex is introduced in to extract features that represent contour information of latent salient objects such as corners, line intersections and line endpoints, which are combined together with brightness, color and orientation features to form the final saliency map. This model is an analog for the processing mechanism of visual signals along from retina, lateral geniculate nucleus(LGN)to primary visual cortex V1:Firstly, according to the characteristics of the retina and LGN, an input image is decomposed into brightness and opposite color channels; Then, the simple cell is simulated with 2D Gabor filters, and the amplitude of Gabor response is utilized to represent the response of complex cell; Finally, the response of an end-stopped cell is obtained by multiplying the response of two complex cells with different orientation, and outputs of V1 and LGN constitute a bottom-up saliency map. Experimental results on public datasets show that our model can accurately predict human fixations, and the performance achieves the state of the art of bottom-up saliency model.

Saliency Detection Based on Multi-Scale Superpixel and Dictionary Learning

2014 ◽  
Vol 701-702 ◽  
pp. 348-351
Author(s):  
Gang Hou ◽  
He Xin Yan ◽  
Fan Zhang ◽  
Hui Rong Hou ◽  
Ming Zhang
Keyword(s):  
Dictionary Learning ◽  
Saliency Detection ◽  
Visual Saliency ◽  
Scale Space ◽  
Detection Algorithm ◽  
Saliency Map ◽  
Reconstruction Error ◽  
Multi Scale ◽  
Training Samples ◽  
Scale Spaces

In recent years, saliency detection has been gaining increasing attention since it could significantly boost many content-based multimedia applications. In this paper, we propose a visual saliency detection algorithm based on multi-scale superpixel and dictionary learning . Firstly, in each scale space, we extract the boundaries as the training samples to learn a dictionary through sparse coding and dictionary learning methods. Then, according to reconstruction error of each superpixel, the saliency map is generated for each scale of superpixel. Finally, some saliency maps from different scale spaces are fused together to generate the final saliency map. The experimental results show that the proposed algorithm can highlight the salient regions uniformly and performs better compared with the other five methods.

Lighter and Faster Cross-Concatenated Multi-Scale Residual Block Based Network for Visual Saliency Prediction

2021 ◽  
Author(s):  
Sai Phani Kumar Malladi ◽  
Jayanta Mukhopadhyay ◽  
Chaker Larabi ◽  
Santanu Chaudhury
Keyword(s):  
Visual Saliency ◽  
Multi Scale ◽  
Saliency Prediction ◽  
Block Based ◽  
Residual Block
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