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.

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 cube-based saliency detection method using integrated visual and spatial features

Sensor Review ◽  
2016 ◽  
Vol 36 (2) ◽  
pp. 148-157 ◽  
Author(s):  
Tao Liu ◽  
Zhixiang Fang ◽  
Qingzhou Mao ◽  
Qingquan Li ◽  
Xing Zhang
Keyword(s):  
Saliency Detection ◽  
Characteristic Curve ◽  
Visual Saliency ◽  
Recall Rate ◽  
Detection Methods ◽  
Content Type ◽  
Spatial Features ◽  
Saliency Model ◽  
3D Scene ◽  
Visual Saliency Model

Purpose The spatial feature is important for scene saliency detection. Scene-based visual saliency detection methods fail to incorporate 3D scene spatial aspects. This paper aims to propose a cube-based method to improve saliency detection through integrating visual and spatial features in 3D scenes. Design/methodology/approach In the presented approach, a multiscale cube pyramid is used to organize the 3D image scene and mesh model. Each 3D cube in this pyramid represents a space unit similar to a pixel in the image saliency model multiscale image pyramid. In each 3D cube color, intensity and orientation features are extracted from the image and a quantitative concave–convex descriptor is extracted from the 3D space. A Gaussian filter is then used on this pyramid of cubes with an extended center-surround difference introduced to compute the cube-based 3D scene saliency. Findings The precision-recall rate and receiver operating characteristic curve is used to evaluate the method and other state-of-art methods. The results show that the method used is better than traditional image-based methods, especially for 3D scenes. Originality/value This paper presents a method that improves the image-based visual saliency model.

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.

Study on Damage Detection of Bridge Based on Wavelet Multi-Scale Analysis

2013 ◽  
Vol 639-640 ◽  
pp. 1010-1014 ◽  
Author(s):  
Ke Ding ◽  
Ting Peng Chen
Keyword(s):  
Wavelet Transform ◽  
Damage Detection ◽  
Detection Method ◽  
Mode Shapes ◽  
Scale Analysis ◽  
Multi Scale ◽  
Damage Location ◽  
Transform Coefficients ◽  
Beam Bridge ◽  
Multi Scale Analysis

The damage detection method based on wavelet multi-scale analysis is presented in the paper. The damage location can be identified by analyzing the multi-scale wavelet transform coefficients of curvatures of mode shapes. The extreme value of wavelet transform coefficients indicates the damage location. But it is difficult to detect the location of defect if the defect is near to the equilibrium position of vibration. In order to solve this problem, we put forward a method which is to add the wavelet transform coefficients of multi modals together. The method can effectively overcome the above problem. Three damage situations of simply supported beam bridge are discussed in the paper. The results show that the peaks of wavelet transform coefficients indicate the damage location of structural. It is possible to pinpoint the damage location based on wavelet multi-scale analysis on curvatures of mode shapes.

A Unified Approach of Object-Level Saliency Detection in Images and Videos

2013 ◽  
Vol 765-767 ◽  
pp. 1401-1405
Author(s):  
Chi Zhang ◽  
Wei Qiang Wang
Keyword(s):  
Quality Evaluation ◽  
Saliency Detection ◽  
Visual Saliency ◽  
Unified Approach ◽  
Feature Maps ◽  
Still Images ◽  
Saliency Maps ◽  
Novel Method ◽  
Object Level ◽  
Important Branch

Object-level saliency detection is an important branch of visual saliency. In this paper, we propose a novel method which can conduct object-level saliency detection in both images and videos in a unified way. We employ a more effective spatial compactness assumption to measure saliency instead of the popular contrast assumption. In addition, we present a combination framework which integrates multiple saliency maps generated in different feature maps. The proposed algorithm can automatically select saliency maps of high quality according to the quality evaluation score we define. The experimental results demonstrate that the proposed method outperforms all state-of-the-art methods on both of the datasets of still images and video sequences.

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