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A deformable CNN-based triplet model for fine-grained sketch-based image retrieval

pattern recognition 2022年 125卷 108508-108508页

作者： Zhang, Xianlin Shen, Mengling Li, Xueming Feng, Fangxiang Beijing Univ Posts & Telecommun Sch Digtal Media & Design Arts Beijing Xitucheng Rd 10 Beijing 100876 Peoples R China Beijing Univ Posts & Telecommun Sch Artificial Intelligence Beijing Xitucheng Rd 10 Beijing 100876 Peoples R China Beijing Univ Posts & Telecommun Beijing Key Lab Network Syst & Network Culture Beijing Xitucheng Rd 10 Beijing 100876 Peoples R China

With the popularity of electronic touch-screen and pressure sensing devices, fine-grained sketch based image retrieval (FG-SBIR) has become a research hotspot. In this paper, we stress the core problems of FG-SBIR: a. how to reduce the difference between the non-homogenous of heterogeneous media, and b. how to improve the distinguishability of sketch features. Specifically, a sketch generation model is first proposed to replace the conventional pre-processing of roughly extracting image edges, moreover, this model can alleviate the dilemma of sketch data scarcity. We then construct a novel FG-SBIR model which takes advantage of deformable convolutional neural network while taking into consideration of semantic attributes together. In addition, we build a fine-grained clothing sketch-image dataset, which has rich attribute annotations, for the first time. Extensive experiments exhibit that our proposed model achieves a better performance in improving the retrieval accuracy over the state-of-the-art baselines. (c) 2021 Elsevier Ltd. All rights reserved.

关键词： Freehand sketches FG-SBIR Semantic attributes Deformable CNNs Preprocessing

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Classification of MRI's images using the Fractional Fourier Transform and the Local Binary pattern information by Artificial Neural Network 29

Classification of MRI's images using the Fractional Fourier ...

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Conference on Infrared remote sensing and Instrumentation XXIX

作者： Estrada Marmolejo, Carlos A. Alvarez-Borrego, Josue Garcia-Torales, Guillermo Flores Nunez, Jorge Luis Univ Guadalajara Av Revoluc 1500 Guadalajara 44430 Jalisco Mexico CICESE Carretera Ensenada Tijuana 3918 Zona Playitas Ensenada 22860 Baja California Mexico

ISBN: (数字)9781510644991

ISBN: (纸本)9781510644991;9781510644984

pattern recognition techniques are widely used in computer vision, classification of radio signals, and voice recognition. The fractional Fourier transform is used to recognize patterns using binary rings masks and segment images. This technique has the characteristic of being invariant to position and rotation and finally obtaining a one-dimensional signature. On the other hand, Neural Networks are used for pattern recognition based on a deep neural network algorithm. It has the characteristic of training large datasets with millions of images. Artificial Neural Networks(ANNs) are used for several applications such as pattern recognition and classification of input data. In particular, the ANN has been used to evaluate medical images from the brain to assess if the image corresponds to Alzheimer's disease. One disadvantage of the neural network is a large amount of time to learn depending on the number of patterns to be identified or classified and the ability to adapt and recognize patterns. Besides, the fractional Fourier transform cannot analyze a large amount of information. In this work, a comparison between the Artificial Neural Network and the Fractional Fourier Transform is presented to determine which will be the best for recognizing a batch of selected medical images. We propose a reconstruction method using both techniques for precise image recognition and the evaluation of their respective metrics such as accuracy, precision, sensitivity, and specificity. The medical images regarding Alzheimer's disease are no dementia, very mild dementia, mild dementia presenting the best perfomance regarding the receiver operating characteristics and moderate dementia was the worst classified related to the number of images of the dataset.

关键词： pattern recognition one-dimensional signature fractional Fourier transform binary rings masks Artificial Neural Network computer vision image processing local binary pattern neural network

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Multiorder Interaction Information Embedding-Based Multiview Fusion-Aided Hyperspectral image Classification

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IEEE GEOSCIENCE AND remote sensing LETTERS 2022年 19卷 1页

作者： Li, Xuefei Cao, Weijia Zhang, Kai Liu, Baodi Tao, Dapeng Liu, Weifeng China Univ Petr East China Coll Oceanog & Space Informat Qingdao 266580 Peoples R China Xidian Univ State Key Lab Integrated Serv Networks Xian 710071 Peoples R China Chinese Acad Sci Aerosp Informat Res Inst Beijing 100101 Peoples R China Univ Macau Dept Comp & Informat Sci Macau 999078 Peoples R China Yangtze Three Gorges Technol & Econ Dev Co Ltd Beijing 100038 Peoples R China China Univ Petr East China Sch Petr Engn Qingdao 266580 Peoples R China China Univ Petr East China Coll Control Sci & Engn Qingdao 266580 Peoples R China Yunnan Univ Sch Informat Sci & Engn Kunming 650091 Yunnan Peoples R China

Hyperspectral images (HSIs) are obtained from hyperspectral imaging sensors, which capture information in hundreds of spectral bands of objects. However, how to take full advantage of spatial and spectral information from many spectral bands to improve the performance of HSI classification remains an open question. Many HSI classification works have recently been reported by employing multiview learning (MVL) algorithms that can fully use complementary information between different view features and thus have received widespread attention. This letter proposes a multiview fusion network based on multiorder interaction information embedding for HSI classification. First, the correlation matrix between spectral bands is used to divide the original data into multiple subsets as local views. The subset after the segmented-PCA process is used as the global view. Second, the features of different views are extracted separately using a feature extraction network and mapped to the same dimension. Prefusion is achieved by multiorder interaction of various view features. Finally, loss-weighted fusion is applied to each view according to its contribution to the classification task. To evaluate the effectiveness of the proposed method, complete experiments were conducted on three commonly used HSI datasets, namely Pavia University, Houston 2013, and Houston 2018. The experimental results demonstrate that the proposed method improves the classification performance of existing feature extraction networks and is more competitive with other methods in the field.

关键词： Feature extraction Correlation Petroleum Principal component analysis Optimization Geoscience and remote sensing Task analysis Hyperspectral image (HSI) classification interaction information loss fusion multiview learning (MVL)

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Analysis and Application of Multispectral Data for Water Segmentation Using Machine Learning

Analysis and Application of Multispectral Data for Water ...

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International Conference on Computer Vision and Machine Intelligence, CVMI 2022

作者： Gupta, Shubham Uma, D. Hebbar, R. PES University KA Bangaluru560085 India Regional Remote Sensing Centre–South ISRO KA Bengaluru560037 India

ISBN: (纸本)9789811978661

Monitoring water is a complex task due to its dynamic nature, added pollutants, and land build-up. The availability of high-resolution data by Sentinel-2 multispectral products makes implementing remote sensing applications feasible. However, overutilizing or underutilizing multispectral bands of the product can lead to inferior performance. In this work, we compare the performances of ten out of the thirteen bands available in a Sentinel-2 product for water segmentation using eight machine learning algorithms. We find that the shortwave-infrared bands (B11 and B12) are the most superior for segmenting water bodies. B11 achieves an overall accuracy of 71 % while B12 achieves 69 % across all algorithms on the test site. We also find that the Support Vector Machine (SVM) algorithm is the most favorable for single-band water segmentation. The SVM achieves an overall accuracy of 69 % across the tested bands over the given test site. Finally, to demonstrate the effectiveness of choosing the right amount of data, we use only B11 reflectance data to train an artificial neural network, BandNet. Even with a basic architecture, BandNet is proportionate to known architectures for semantic and water segmentation, achieving a 92.47 mIOU on the test site. BandNet requires only a fraction of the time and resources to train and run inference, making it suitable to be deployed on web applications to run and monitor water bodies in localized regions. Our codebase is available at https://***/IamShubhamGupto/BandNet. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Neural networks

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Content-based image Retrieval in Cyber-Physical System Modeling of Robots 11th

Content-based Image Retrieval in Cyber-Physical System Model...

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11th International Conference on Recent Trends in Computing, ICRTC 2022

作者： Anantha Prabha, P. Subashree, B. Deva Priya, M. Department of Computer Science and Engineering Sri Krishna College of Technology Tamil Nadu Coimbatore India Department of Computer Science and Engineering Sri Eshwar College of Engineering Tamil Nadu Coimbatore India

ISBN: (纸本)9789811988240

Cyber-Physical System (CPS) is used in industries and automated plants as they have modern administration abilities and are capable of performing real-time processing in a distributed architecture. In mechanical plants, they are expected to perform image recognition and retrieval, and other Machine Learning (ML) processes for efficient functioning and screening of assembling units and products. The existing strategies like remote sensing retrieval and the Gaussian mixture model for image Retrieval (IR) are not suitable for huge datasets. The drawbacks of the existing strategies can be eliminated based on features of data by using Improvised Density Scale-Invariant Feature Transform (ID-SIFT). This approach mainly focuses on geometrical features like color, texture, size and shape-based image matching offering an improved accuracy of 89%. This approach is relatively efficient when compared to existing algorithms used in IR. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Cyber Physical System

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SAR-Optical image Matching Model Based on Contrastive Learning 20

SAR-Optical Image Matching Model Based on Contrastive Learni...

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20th International Computer Conference on Wavelet Active Media Technology and Information processing, ICCWAMTIP 2023

作者： Yaoting, Xiong School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9798350318982

Due to the strong complementary nature of synthetic aperture radar (SAR) and optical imagery in remote sensing, matching SAR images with optical images enables the complementary extraction of ground surface feature information. This approach is beneficial for addressing tasks such as change detection, target recognition, and land cover classification. With the development of deep learning, most current models for SAR-optical image matching utilize siamese neural networks and have achieved good matching performance. However, in practical applications, test data is often unknown, and there may be differences between training and test data. A matching model that performs well on the training dataset may not generalize well to the test data. Domain generalization research for heterogeneous image matching aims to improve the generalization performance of matching models on unknown test domains, and this task has practical significance and application prospects. In this paper, we propose a contrastive learning-based SAR-optical image matching model specifically designed to enhance the generalization performance of matching models trained on multiple datasets. When dealing with cross-domain scenarios, enforcing feature consistency of matching points preserves more information conducive to matching. We propose a contrastive learning method to limit the feature consistency of matching points, thereby improving the generalization performance of cross-source image matching models. © 2023 IEEE.

关键词： image matching

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A Semantic Segmentation Method of Buildings in remote sensing image Based on Improved UNet 2

A Semantic Segmentation Method of Buildings in Remote Sensin...

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2nd International Conference on Signal image processing and Communication, ICSIPC 2022

作者： Li, Zhongyu Liu, Yang Kuang, Yin Wang, Huajun Liu, Cheng College of Computer Science Chengdu Normal University Chengdu611130 China College of Geophysics Chengdu University of Technology Chengdu610059 China Key Laboratory of Pattern Recognition and Intelligent Information Processing of Sichuan Chengdu University Chengdu610106 China Artificial Intelligence Key Laboratory of Sichuan Province Zigong643000 China Key Laboratory of interior Layout optimization and Security Institutions of Higher Education of Sichuan Province Chengdu Normal University Sichuan Chengdu611130 China College of Movie and Media Sichuan Normal University Chengdu610066 China

ISBN: (纸本)9781510657694

Aiming at the problem of model instability and overfitting of deep neural networks with the deepening of the number of network layers, the current mainstream method is to use batch normalization (BN) to alleviate them. However, since the BN method is more sensitive to batch size when the batch size is small, the model performance will be poor. For a relatively large model, due to the limitation of video memory, the batch size cannot take a large value, limiting the model's performance. Because of the dependence of BN on batch size, this paper adopts group normalization (GN) instead of batch normalization (BN) in the UNet network to alleviate the impact of the model on batch size. Then experiments are carried out on the WHUBuilding dataset. The experimental results show that the improved model (UNet-GN) improves the mean intersection over union (MIoU) and mean pixel accuracy (MPA) by 10.66% and 1.65% respectively compared with the original model (UNet-BN). © 2022 SPIE.

关键词： Deep neural networks

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Fractional Fourier-Based Frequency-Spatial-Spectral Prototype Network for Agricultural Hyperspectral image Open-Set Classification

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IEEE TRANSACTIONS ON GEOSCIENCE AND remote sensing 2024年 62卷 1页

作者： Chen, Maoyang Feng, Shou Zhao, Chunhui Qu, Bo Su, Nan Li, Wei Tao, Ran Harbin Engn Univ Coll Informat & Commun Engn Harbin 150001 Peoples R China Harbin Engn Univ Key Lab Adv Marine Commun & Informat Technol Minist Ind & Informat Technol Harbin 150001 Peoples R China Shaanxi Key Lab Opt Remote Sensing & Intelligent I Xian 710119 Peoples R China Beijing Inst Technol Sch Informat & Elect Beijing 100081 Peoples R China Chinese Acad Sci Xian Inst Opt & Precis Mech Key Lab Spectral Imaging Technol CAS Xian 710119 Shaanxi Peoples R China Xi An Jiao Tong Univ Key Lab Intelligent Networks & Network Secur Minist Educ Xian 710049 Peoples R China Beijing Inst Technol Sch Informat & Elect Beijing 100081 Peoples R China

At present, hyperspectral image classification (HSIC) technology has been warmly concerned in all walks of life, especially in agriculture. However, existing classification methods operate under the closed-set assumption, which deviates from the real world with open properties. At the same time, there are more serious phenomena of different crops with similar spectrums and the same crops with different spectrum in agricultural hyperspectral data, which is also a great challenge to existing methods. In this work, a fractional Fourier-based frequency-spatial-spectral prototype network (FrFSSPN) is proposed to address the challenges of open-set HSIC in agricultural scenarios. First, fractional Fourier transform (FrFT) is introduced into the network to combine the information in the frequency domain with the spatial-spectral information, so as to expand the difference between different classes on the premise of ensuring the similarity between classes. Then, the prototype learning strategy is introduced into the network to improve the feature recognition capability of the network through prototype loss. Finally, in order to break the stubbornly closed-set property of the closed-set classification (CSC) method, the open-set recognition module is proposed. The difference between the prototype vector and the feature vector is used to judge the unknown class. Experiments on three agricultural hyperspectral datasets show that this method can effectively identify unknown classes without sacrificing the classification accuracy of closed-set, and has satisfactory classification performance.

关键词： Agricultural hyperspectral image classification (HSIC) fractional Fourier transform (FrFT) open-set classification (OSC) prototype learning

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Deblurring of Beamformed images in the Ocean Acoustic Waveguide Using Deep Learning-Based Deconvolution

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remote sensing 2024年第13期16卷 2411页

作者： Zha, Zijie Yan, Xi Ping, Xiaobin Wang, Shilong Wang, Delin Zhejiang Univ Ocean Coll Zhoushan 316021 Peoples R China Key Lab Ocean Observat Imaging Testbed Zhejiang Pr Zhoushan 316000 Peoples R China

A horizontal towed linear coherent hydrophone array is often employed to estimate the spatial intensity distribution of incident plane waves scattered from the geological and biological features in an ocean acoustic waveguide using conventional beamforming. However, due to the physical limitations of the array aperture, the spatial resolution after conventional beamforming is often limited by the fat main lobe and the high sidelobes. Here, we propose a method originated from computer vision deblurring based on deep learning to enhance the spatial resolution of beamformed images. The effect of image blurring after conventional beamforming can be considered a convolution of beam pattern, which acts as a point spread function (PSF), and the original spatial intensity distributions of incident plane waves. A modified U-Net-like network is trained on a simulated dataset. The instantaneous acoustic complex amplitude is assumed following circular complex Gaussian random (CCGR) statistics. Both synthetic data and experimental data collected from the South China Sea Experiment in 2021 are used to illustrate the effectiveness of this approach, showing a maximum 700% reduction in a 3 dB width over conventional beamforming. A lower normalized mean square error (NMSE) is provided compared with other deconvolution-based algorithms, such as the Richardson-Lucy algorithm and the approximate likelihood model-based deconvolution algorithm. The method is applicable in various acoustic imaging applications that employ linear coherent hydrophone arrays with one-dimensional conventional beamforming, such as ocean acoustic waveguide remote sensing (OAWRS).

关键词： acoustic remote sensing deconvolution conventional beamforming image processing ocean acoustic waveguide remote sensing

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Prediction of Fish Location by Combining Fisheries Data and Sea Bottom Temperature Forecasting 21st

Prediction of Fish Location by Combining Fisheries Data and ...

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21st International Conference on image Analysis and processing (ICIAP)

作者： Ospici, Matthieu Sys, Klaas Guegan-Marat, Sophie Atos CVLab F-38130 Echirolles France Flanders Res Inst Agr Fisheries & Food Anim Sci Unit Ankerstr 1 B-8400 Oostende Belgium

ISBN: (纸本)9783031064333;9783031064326

This paper combines fisheries dependent data and environmental data to be used in a machine learning pipeline to predict the spatiotemporal abundance of two species (plaice and sole) commonly caught by the Belgian fishery in the North Sea. By combining fisheries related features with environmental data, sea bottom temperature derived from remote sensing, a higher accuracy can be achieved. In a forecast setting, the predictive accuracy is further improved by predicting, using a recurrent deep neural network, the sea bottom temperature up to four days in advance instead of relying on the last previous temperature measurement.

关键词： Computer vision Machine learning Spatiotemporal modelling Fisheries remote sensing data

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