scholarly journals Visual Saliency Model Using Sift and Comparison of Learning Approaches

2014 ◽  
Author(s):  
Hamdi Yalin Yalic
Keyword(s):  
Visual Saliency ◽  
Learning Approaches ◽  
Saliency Model ◽  
Visual Saliency Model

scholarly journals Visual Saliency Model-Based Image Watermarking with Laplacian Distribution

Information ◽  
2018 ◽  
Vol 9 (9) ◽  
pp. 239
Author(s):  
Hongmei Liu ◽  
Jinhua Liu ◽  
Mingfeng Zhao
Keyword(s):  
Simulation Analysis ◽  
Image Watermarking ◽  
Additive White Gaussian Noise ◽  
Visual Saliency ◽  
High Energy ◽  
Distribution Model ◽  
Saliency Model ◽  
Detection Approach ◽  
Laplacian Distribution ◽  
Visual Saliency Model

To improve the invisibility and robustness of the multiplicative watermarking algorithm, an adaptive image watermarking algorithm is proposed based on the visual saliency model and Laplacian distribution in the wavelet domain. The algorithm designs an adaptive multiplicative watermark strength factor by utilizing the energy aggregation of the high-frequency wavelet sub-band, texture masking and visual saliency characteristics. Then, the image blocks with high-energy are selected as the watermark embedding space to implement the imperceptibility of the watermark. In terms of watermark detection, the Laplacian distribution model is used to model the wavelet coefficients, and a blind watermark detection approach is exploited based on the maximum likelihood scheme. Finally, this paper performs the simulation analysis and comparison of the performance of the proposed algorithm. Experimental results show that the proposed algorithm is robust against additive white Gaussian noise, JPEG compression, median filtering, scaling, rotation attack and other attacks.

A Novel Biologically Inspired Visual Saliency Model

2014 ◽  
Vol 6 (4) ◽  
pp. 841-848 ◽  
Author(s):  
Jingjing Zhao ◽  
Shujin Sun ◽  
Xingtong Liu ◽  
Jixiang Sun ◽  
Afeng Yang
Keyword(s):  
Visual Saliency ◽  
Biologically Inspired ◽  
Saliency Model ◽  
Visual Saliency Model

A Two-Stage Clustering Based 3D Visual Saliency Model for Dynamic Scenarios

2019 ◽  
Vol 21 (4) ◽  
pp. 809-820 ◽  
Author(s):  
You Yang ◽  
Bei Li ◽  
Pian Li ◽  
Qiong Liu
Keyword(s):  
Visual Saliency ◽  
Two Stage ◽  
Saliency Model ◽  
Visual Saliency Model

Local and Global Contrast for Saliency Estimate in DCT Domain

2013 ◽  
Vol 456 ◽  
pp. 611-615
Author(s):  
Nan Ping Ling ◽  
Han Ling Zhang
Keyword(s):  
State Of The Art ◽  
Visual Saliency ◽  
Local Contrast ◽  
Global Contrast ◽  
Contrast Method ◽  
Saliency Model ◽  
Dct Domain ◽  
Proposed Model ◽  
Roc Area ◽  
Visual Saliency Model

In this paper, we present a new bottom-up visual saliency model, which utilizes local and global contrast method to calculate the saliency in DCT domain. Our proposed method is firstly used in the DCT domain. The local contrast method uses the center-surround operation to compute the local saliency, and the global contrast method calculate the dissimilarity between DCT blocks of image and any other DCT blocks in any location. The final saliency is generated by combining the local with global contrast saliency. Experimental evaluation on a publicly available benchmark dataset shows the proposed model can acquire state-of-the-art results and outperform the other models in terms of the ROC area.

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.

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