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An adaptive multi-sensor visual attention model

NEURAL COMPUTING & APPLICATIONS 2022年第9期34卷 7241-7252页

作者： Chen, Wenbai Li, Jingchen Shi, Haobin Hwang, Kao-Shing Beijing Informat Sci & Technol Univ Automat Sch Beijing 100192 Peoples R China Northwestern Polytech Univ Sch Comp Sci Xian 710072 Shaanxi Peoples R China Natl Sun Yat Sen Univ Dept Elect Engn Kaohsiung 80424 Taiwan

The emerging recurrent visual attention models mostly utilize a sensor to continuously capture features from the input, which requires a suited design for the sensor. Researchers usually need a number of attempts to determine optimal structures for the sensor and corresponding modules. In this work, an adaptive multi-sensor visual attention model (AM-MA) is proposed to enhance the recurrent visual attention model. The proposed model uses several sensors to observe the original input recurrently, while the number of sensors can be added adaptively. Each sensor generates a hidden state and is followed by a location network to provide the deployment scheme. We design a self-evaluation mechanism for AM-MA, by which it can decide whether to add new sensors during training. Besides, the proposed AM-MA leverages a fine-tune mechanism to avoid a lengthy training process. AM-MA is a parameter-insensitive model. That is, there is no need for researchers to pre-train the model for finding the optimal structure in the case of unknown complexity. Experimental results show that the proposed AM-MA not only outperforms the renowned sensor-based attention model on image classification tasks, but also achieves satisfactory results when given an inappropriate structure.

关键词： attention mechanism visual attention model Neural network

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Alzheimer's disease diagnosis based on the visual attention model and equal-distance ring shape context features

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IET IMAGE PROCESSING 2021年第10期15卷 2351-2362页

作者： Lao, Huan Zhang, Xuejun Tang, Yanyan Liang, Chan Guangxi Univ Med Coll Nanning Peoples R China Guangxi Univ Sch Comp Elect & Informat Nanning 530004 Guangxi Peoples R China Guangxi Key Lab Multimedia Commun & Network Techn Nanning Peoples R China Guangxi Med Univ Affiliated Hosp 1 Dept Neurol Nanning Peoples R China

Alzheimer's disease (AD) is an irreversible neurodegenerative disease caused by rapid degeneration of brain cells. More and more researchers focus on effective and accurate methods for the diagnosis of AD. In this paper, a method to identify AD by extracting equal-distant ring shape context features from saliency map of structural magnetic resonance imaging (sMRI) is proposed. The experimental results on the thin-layer MR images of the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset showed that our method helped improve the performance of identifying brain diseases. Specifically, the classification accuracy of 94.83% for AD versus CN, 98.31% for AD versus MCI and 85.77% for MCI versus CN, respectively. At the same time, experiments on Open Access Series of Imaging Studies dataset and clinically collected thick-layer MR images verify the classification performance of the method. The results show that this method may have higher application value in clinical application, with classification accuracies of 96.56% and 98.18% for AD versus CN, respectively. Compared with the methods based on gray matter (GM) density, cortical thickness and hippocampal volume, our method achieved higher accuracy of AD (or MCI) and CN classification.

关键词： saliency map neurophysiology brain cells visual attention model MCI versus CN patient diagnosis classification performance feature extraction brain diseases equal-distant ring shape context features thin-layer MR images thick-layer MR images classification accuracy brain biomedical MRI structural magnetic resonance imaging effective methods medical image processing equal-distance ring shape context features irreversible neurodegenerative disease Alzheimer's disease diagnosis rapid degeneration image classification Imaging Studies dataset diseases

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Multi-Difference Image Fusion Change Detection Using a visual attention model on VHR Satellite Data

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REMOTE SENSING 2023年第15期15卷 3799-3799页

作者： Luo, Jianhui Chen, Qiang Wang, Lei Huang, Yixiao Beijing Univ Civil Engn & Architecture Sch Geomatics & Urban Spatial Informat Beijing 102616 Peoples R China Beijing Univ Civil Engn & Architecture Key Lab Urban Spatial Informat Minist Nat Resources Beijing 102616 Peoples R China

For very-high-resolution (VHR) remote sensing images with complex objects and rich textural information, multi-difference image fusion has been proven as an effective method to improve the performance of change detection. However, errors are superimposed during this process and a single spectral feature cannot fully utilize the correlation between pixels, resulting in low robustness. To overcome these problems and optimize the performance of multi-difference image fusion in change detection, we propose a novel multi-difference image fusion change detection method based on a visual attention model (VA-MDCD). First, we construct difference images using change vector analysis (CVA) and spectral gradient difference (SGD). Second, we use the visual attention model to calculate multiple color, intensity and orientation features of the difference images to obtain the difference saliency images. Third, we use the wavelet transform fusion algorithm to fuse two saliency images. Finally, we execute the OTSU threshold segmentation algorithm (OTSU) to obtain the final change detection map. To validate the effectiveness of VA-MDCD on VHR images, two datasets of Jilin 1 and Beijing 2 are selected for experiments. Compared with classical methods, the proposed method has a better performance with fewer missed alarms (MA) and false alarms (FA), which proves that the method has a strong robustness and generalization ability. The F-measure of the two datasets is 0.6671 and 0.7313, respectively. In addition, the results of ablation experiments confirm that the three feature extraction modules of the model all play a positive role.

关键词： very high resolution (VHR) change detection multi-difference image fusion visual attention model feature extraction

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RETRACTED: Influence of promotion mode on purchase decision based on multilevel psychological distance dimension of visual attention model and data mining (Retracted article. See vol. 35, 2023)

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2021年第12期33卷

作者： Tang, Tiantian Hu, Pei Wu, Ge Southwest Jiaotong Univ Sch Econ & Management Chengdu 610031 Sichuan Peoples R China

In the era of rapid development of information technology, e-commerce has opened up a different cross-distance trade model from traditional trade and has changed the traditional market competition. With the rapid development of network information technology and the rapid spread of online shopping, online shopping will gradually become one of the main ways people buy. It is a decision-making process for a customer to purchase a product to characterize the product, evaluate the characteristics of the product, and select a product that matches its needs. For online merchants, it is especially important to make a profit in online sales and to be in a competitive position. This paper studies the impact of promotional methods based on visual attention models and data mining on purchasing decisions. The revised Itti visual saliency model is designed considering the visual features;the machine learning algorithm is integrated to construct the comprehensive structure of the model. The experimental results show the robustness and efficiency of the designed framework.

关键词： data analysis data mining information collection multilevel model visual attention model

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Detecting Maritime Infrared Targets in Harsh Environment by Improved visual attention model Preselector and Anti-Jitter Spatiotemporal Filter Discriminator

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REMOTE SENSING 2022年第20期14卷 5213-5213页

作者： Ma, Dongdong Dong, Lili Xu, Wenhai Dalian Maritime Univ Sch Informat Sci & Technol Dalian 116026 Peoples R China

Without any prior knowledge, it has always been a serious challenge to accurately detect infrared targets under a maritime harsh environment (MHE). To solve this problem, the main contribution of this paper is to use the improved visual attention model (VAM) preselector and anti-jitter spatiotemporal filter (ASF) discriminator to detect infrared targets in the MHE. The proposed method consists of image preprocessing, the single frame detection of suspected targets and a multi-frame judgment of real targets. First, in the process of single-frame image processing, a combination of the Gaussian difference filtering and local minimum filtering is applied to overcome the uneven background brightness distribution and improve the saliency of the target. Second, an intensity standard deviation method is designed to determine the unevenness of the background. According to the difference in background smoothness, an appropriate "center-surround difference" operation is selected to suppress sea wave interference, and the single frame suspected target candidate region is obtained. Third, in order to "align" the multi-frame image sequence, a method for correcting the position of the inter-frame jitter is proposed. The inter-frame jitter is measured and compensated by the inter-frame block matching results. Finally, according to the three assumptions of multi-frame spatiotemporal filtering, single-frame false targets are filtered out and combined with the OTSU method to segment the real target area. To evaluate the performance index of the proposed method by comparing it with the four other state-of-the-art methods for dealing with the MHE. The experimental results show that the algorithm achieves the maximum detection rate (DR) on the premise of being far lower than the false alarm rate (FAR) of the comparison method. The final experimental results also confirmed that the proposed algorithm is more suitable for infrared target detection in diverse MHEs.

关键词： infrared target detection visual attention model multi-frame spatiotemporal filtering image segmentation

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Robust image hashing with visual attention model and invariant moments

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IET IMAGE PROCESSING 2020年第5期14卷 901-908页

作者： Tang, Zhenjun Zhang, Hanyun Pun, Chi-Man Yu, Mengzhu Yu, Chunqiang Zhang, Xianquan Guangxi Normal Univ Guangxi Key Lab Multisource Informat Min & Secur Guilin 541004 Peoples R China Guangxi Normal Univ Dept Comp Sci Guilin 541004 Peoples R China Univ Macau Dept Comp & Informat Sci Macau 999078 Peoples R China

Image hashing is an efficient technique of multimedia processing for many applications, such as image copy detection, image authentication, and social event detection. In this study, the authors propose a novel image hashing with visual attention model and invariant moments. An important contribution is the weighted DWT (discrete wavelet transform) representation by incorporating a visual attention model called Itti saliency model into LL sub-band. Since the Itti saliency model can efficiently extract saliency map reflecting regions of attention focus, perceptual robustness of the proposed hashing is achieved. In addition, as invariant moments are robust and discriminative features, hash construction with invariant moments extracted from the weighted DWT representation ensures good classification performance between robustness and discrimination. Extensive experiments with open image datasets are done to validate the performances of the proposed hashing. The results demonstrate that the proposed hashing is robust and discriminative. Performance comparisons with some hashing algorithms are also conducted, and the receiver operating characteristic results illustrate that the proposed hashing outperforms the compared hashing algorithms in classification performance between robustness and discrimination.

关键词： feature extraction wavelet transforms image coding image classification robot vision discrete wavelet transforms cryptography robust image hashing visual attention model invariant moments image copy detection image authentication social event detection Itti saliency model attention focus perceptual robustness authors weighted DWT representation open image datasets compared hashing algorithms

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Subway Rail Fastener Locating Based on visual attention model 13

Subway Rail Fastener Locating Based on Visual Attention Mode...

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13th Asia Pacific Transportation Development Conference - Resilience and Sustainable Transportation Systems

作者： Qi, Wei-Wei Zheng, Shu-Bin Chen, Xing-Jie Li, Li-Ming Shanghai Univ Engn Sci Sch Urban Railway Transportat Shanghai Peoples R China

ISBN: (数字)9780784482902

ISBN: (纸本)9780784482902

Fastener defects detection based on computer vision is an important task in subway rail inspection systems, in which the first step is fastener location. In this paper, a fastener locating method based on visual attention model for subway track is presented, which can implement fastener location in the subway track mixed with ballast-less track in the main line and ballast track on turnout region. Firstly, rail location is obtained by the vertical projection algorithm. Secondly, the image is classified into ballast or ballast-less based on visual attention model with the fractal dimension. Thirdly, ballast image and ballast-less image are treated separately. On one hand, in the ballast image, the sleeper is located with the line segment detector algorithm, and then fastener can be located by the sleeper location combined with the rail location. On the other hand, in the ballast-less image, the fastener is located with horizontal projection algorithm combined with the rail location. Experimental results demonstrate that the proposed method can locate the fastener accurately.

关键词： Fastener Location visual attention model Fractal Dimension Projection Algorithm Line Segment Detector Algorithm

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Infrared target detection in backlighting maritime environment based on visual attention model

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INFRARED PHYSICS & TECHNOLOGY 2019年第0期99卷 193-200页

作者： Dong, Lili Ma, Dongdong Qin, Ge Zhang, Tong Xu, Wenhai Dalian Maritime Univ Sch Informat Sci & Technol Dalian 116026 Peoples R China

The backlighting environment (BE) is a common condition in the process of detecting maritime targets. The reason for the phenomenon of backlighting at maritime is that when the weather is sunny, the target at sea is just between the sunlight and the infrared camera, which causes the radiation energy of sunlight reflected into the infrared camera through the sea surface to be much larger than that reflected into the infrared camera through the target. Therefore, the gray value of the background of the infrared image is larger than the gray value of the target in the BE. In view of this situation, the main content of this paper is to design a robust infrared maritime target detection algorithm based on visual attention model (VAM) by analyzing the characteristics of the target in the BE. First and foremost, a preprocessing method of Gauss difference is proposed to improve the gray value and contrast of the target in strong ocean waves backlighting image. Secondly, a suitable modality will be automatically selected according to the intensity of different ocean waves. Moreover, we adopt the center-surround difference (CSD) that highlight the saliency of the target instead of the CSD at multiple scales in the VAM. Finally, the saliency map is segmented by an adaptive threshold method OTSU, and the integrity of the target area is guaranteed. Thus, the goal of successfully detecting the target is achieved. Experimental results show that the proposed algorithm remarkably improve the accuracy and efficiency of searching maritime targets in the BE.

关键词： Backlighting environment Infrared maritime target detection visual attention model Image segmentation

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Robust image hashing via visual attention model and ring partition

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MATHEMATICAL BIOSCIENCES AND ENGINEERING 2019年第5期16卷 6103-6120页

作者： Tang, Zhenjun Yu, Yongzheng Zhang, Hanyun Yu, Mengzhu Yu, Chunqiang Zhang, Xianquan Guangxi Normal Univ Guangxi Key Lab Multisource Informat Min & Secur Guilin 541004 Peoples R China Guangxi Normal Univ Dept Comp Sci Guilin 541004 Peoples R China

Robustness is an important property of image hashing. Most of the existing hashing algorithms do not reach good robustness against large-angle rotation. Aiming at this problem, we jointly exploit visual attention model and ring partition to design a novel image hashing, which can make good rotation robustness. In the proposed image hashing, a visual attention model called PFT (Phase spectrum of Fourier Transform) model is used to detect saliency map of preprocessed image. The LL sub-band of saliency map is then divided into concentric circles invariant to rotation by ring partition, and the means and variances of DWT coefficients on concentric circles are taken as image features. Next, these features are encrypted by a chaotic map and the Euclidean distances between normalized encrypted features are finally exploited to generate hash. Similarity between hashes is measured by L-1 norm. Many experimental tests show that our image hashing is robust to digital operations including rotation and reaches good discrimination. Comparisons demonstrate that classification performance of our image hashing outperforms those of some well-known hashing algorithms in terms of receiver operating characteristics curves. Simulation of image copy detection is carried out on an open image database called UCID and the result validates effectiveness of our hashing.

关键词： image hashing visual attention model ring partition saliency map image copy detection

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Objective evaluation of fabric pilling based on data-driven visual attention model

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INTERNATIONAL JOURNAL OF CLOTHING SCIENCE AND TECHNOLOGY 2018年第2期30卷 210-221页

作者： Guan, Shengqi Li, Wensen Wang, Jie Lei, Ming Xian Polytech Univ Coll Mech & Elect Engn Xian Shaanxi Peoples R China

Purpose - The purpose of this paper is to develop a new objective evaluation method of fabric pilling using data-driven visual attention model. Design/methodology/approach - First, the multi-scale filtering images are formed by Gaussian pyramid decomposition. Second, center-surround differences algorithm is used between multi-scale filtering images to build saliency map. On this basis, the pilling information is segmented from saliency map by the segmentation threshold. Finally, the pilling is objectively evaluated by extracting pilling feature. Experimental result shows that compared with the traditional detection methods, the proposed objective evaluation method has strong anti-interference ability, and correct classification rate (CCR) is 96 percent. Findings - Fabric pilling saliency can be effectively improved by data-driven visual attention model, which will lead to stronger anti-interference ability and higher correct classification rate. Originality/value - To void uneven illumination, noise, and texture interference, the proposed method can enhance the saliency of small targets in saliency map using a bottom-up visual attention model. Through the threshold segmentation according to pilling feature, the pilling information is effectively from the fabric texture. Pilling feature about pilling area density is extracted to pilling grade evaluation.

关键词： Objective evaluation Threshold segmentation Fabric pilling visual attention model

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