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A Hybrid CNN-Tree Based Model for Enhanced image Classification Performance

A Hybrid CNN-Tree Based Model for Enhanced Image Classificat...

引用

IEEE Signal processing and communications Applications (SIU)

作者： Musa Aydın Zeki Kuş Zeliha Kaya Akçelik Fatih Sultan Mehmet Vakıf Üniversitesi Bilgisayar Mühendisliği ˙Istanbul Türkiye

ISBN: (数字)9798350388961

ISBN: (纸本)9798350388978

Blood cells play an essential role in various bodily functions, such as protection against infections and the body’s defense. The accurate classification of blood cells, generally grouped as red, white, and platelets is important for clinical diagnosis and hematological analysis. However, identifying these cells is a specialized and time-consuming process. Therefore, there is a hot-topic for high-precision automatic blood cell classification methods. Convolutional neural networks (CNNs) are a deep learning model used for visual data analysis and are very powerful in extracting features from data. In this study, we propose a hybrid classification model that combines the feature extraction power of CNNs with the ensemble-based prediction capabilities of Random Forest and XGBoost algorithms. The proposed hybrid model is compared with different methods on the BloodMNIST dataset in terms of classification performance and inference time. The results show that the tree-based methods outperform CNN by up to 8.49 and 11.62 points and achieve up to 82.9 times better inference times than other methods.

关键词： visualization Signal processing algorithms Cells (biology) Signal processing Predictive models Feature extraction Prediction algorithms

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Single image Super-Resolution Using ConvNeXt

Single Image Super-Resolution Using ConvNeXt

引用

IEEE visual communications and image processing (VCIP)

作者： Chenghui You Chaoqun Hong Lijuan Liu Xuehan Lin School of Computer and Information Engineering Xiamen University of Technology Xiamen China

ISBN: (纸本)9781665475938

In recent years, a lot of deep convolution neural networks have been successfully applied in single image super-resolution (SISR). Even in the case of using small convolution kernel, those methods still require large number of parameters and computation. To tackle the problem above, we propose a novel framework to extract features more efficiently. Inspired by the idea of deep separable convolution, we improve the standard residual block and propose the inverted bottleneck block (IBNB). The IBNB replaces the small-sized convolution kernel with the large-sized convolution kernel without introducing additional computation. The proposed IBNB proves that large kernel size convolution is available for SISR. Comprehensive experiments demonstrate that our method surpasses most methods by up to 0.10 ~ 0.32dB in quantitative metrics with fewer parameters.

关键词： Measurement Convolution visual communication Fuses Superresolution Neural networks Feature extraction

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Automatic SPECT image processing for Parkinson’s Disease Early Detection 4th

Automatic SPECT Image Processing for Parkinson’s Disease Ea...

引用

4th International conference on Smart Applications and Data Analysis, SADASC 2022

作者： Boucherouite, Jihad Jilbab, Abdelilah Jbari, Atman E2SN ENSAM Mohammed V University in Rabat Rabat Morocco

ISBN: (纸本)9783031204890

The absence of an effective cure to Parkinson as a neurodegenerative disease calls for early diagnosis and appropriate therapeutic process to provide patients with better quality of treatment. Therefore, to diagnose Parkinson’s Disease (PD) at its early stage, clinicians rely on visual observation of dopaminergic deficit in both caudate and putamen in the striatum region of the brain. SPECT images (Single Photon Emission Computed Tomography) are among functional neuroimaging scans that can show putamen and caudate, and hence help visualizing Dopamine deficit. In this work, we developed an automatic SPECT image model to classify patients as Healthy Control (HC) or Early PD, starting from Dicom SPECT images from PPMI (Parkinson’s Progression Markers Initiative) database to Machine learning classification. The approach we proposed starts with image processing of SPECT images, then extraction of boundary, radial, Striatal Binding Ratio (SBR) and threshold features, then classification using Support Vector Machine (SVM). To the best of our knowledge, no work in the literature has used this combination of the mentioned features together in the classification model. The use of this combination demonstrates promising results. We used a database of 526 images, with 130 HC and 396 PD. The results of our approach show that the Medium Gaussian SVM has a high performance with an accuracy of 97.3%, sensitivity of 95.3%, and specificity of 98%. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Single photon emission computed tomography

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An Efficient Content-aware Downsampling-based Video Compression Framework

An Efficient Content-aware Downsampling-based Video Compress...

引用

IEEE visual communications and image processing (VCIP)

作者： Hao Jiang Li Chen Institute of Image Communication and Network Engineering Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9781665475938

Recently, deep learning-based video compression algorithms have achieved competitive performance in Bjøntegaard delta (BD) rate, especially those adopting super-resolution networks as post-processing modules in downsampling-based video compression (DBC) frameworks. However, limited by the non-differentiable characteristics of traditional codecs, DBC frameworks mainly focus on improving the performance of super-resolution modules while ignoring optimizing downscaling modules. It is crucial to improve video compression performance without introducing additional modifications to the decoder client in practical application scenarios. We propose a context-aware processing network (CPN) compatible with standard codecs with no computational burden introduced to the client, which preserves the critical information and essential structures during downscaling. The proposed CPN works as a precoder cascaded by standard codecs to improve the compression performance on the server before encoding and transmission. Besides, a surrogate codec is employed to simulate the degradation process of the standard codecs and backpropagate the gradient to optimize the CPN. Experimental results show that the proposed method outperforms latest pre-processing networks and achieves considerable performance compared with the latest DBC frameworks.

关键词： Degradation Codecs image coding Quantization (signal) visual communication Superresolution Video compression

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image Enhancement for High-Resolution visual Contents

Image Enhancement for High-Resolution Visual Contents

引用

International conference on Electronics, Information and communications (ICEIC)

作者： Heunseung Lim Jaehee Lee Hyuncheol Kim Heungmin Oh Joonki Paik The Graduate School of Advanced Imaging Science Multimedia & Film Chung-Ang University Seoul Korea 4by4 Incorporated Pixell Lab Seoul Korea The Graduate School of Artificial Intelligence Seoul Korea

This paper proposes an image enhancement method using gamma neural networks and exponential transformation. When acquiring an image, degradation occurs in very many imaging systems, and the quality of the image acquired by surrounding environmental factors decreases due to the combination of deteriorating elements. Alternatively, work that facilitates post-treatment may be performed by artificially deteriorating for post-treatment directly. However, if the information on these additional tasks is not known, there is a problem that the post-processing process is expensive or additional degradation occurs. To solve this problem, this paper uses a neural network that estimates gamma maps through residual learning for images that require post-processing, and finally applies exponential transformations to perform contrast improvement. The contrast improvement method proposed through the experimental results provides an image with less color distortion compared to the existing method.

关键词： Degradation visualization image color analysis Neural networks Imaging Distortion Environmental factors

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Using Convolutional Neural Networks for image Recognition in IoT Applications

Using Convolutional Neural Networks for Image Recognition in...

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Pervasive Computational Technologies (ICPCT), International conference on

作者： Durgam Rajababu Sampenga Veerraju K. Suresh Kumar Sonali Gupta V B Thurai Raaj Rahul Joshi Department of EEE School of Engineering SR University Warangal Telangana India Department of Electronics and Communications Engineering Dadi Institute of Engineering and Technology Anakapalli Visakhapatnam Andhra Pradesh MBA Department Panimalar Engineering College Varadarajapuram Poonamallee Chennai J C Bose University of Science and Technology YMCA Faridabad Electrical and Electronics Engineering Madanapalle Institute of Technology & Science Madanapalle Andhra Pradesh Department of Journalism & Mass Communication SMeH Manav Rachna International Institute of Research and Studies Haryana India

ISBN: (数字)9798331508685

ISBN: (纸本)9798331519476

Convolutional Neural Networks (CNNs) have grown into a powerful picture identification tool. They are an important part of making Internet of Things (IoT) apps. In this regard, CNNs make it possible to analyse visual data gathered from networked devices, like cameras and sensors, effectively and accurately. With an emphasis on its potential for real-time applications for example smart surveillance, healthcare monitoring, autonomous cars, and industrial automation, this study investigates the incorporation of CNN-based image identification into IoT contexts. We tackle important issues such as the requirement for low-latency processing, energy economy, and computing limitations on edge devices. In order to maximise CNN performance in the resource-constrained IoT ecosystem, strategies like model compression, edge computing, and distributed architectures are covered. The ability to interpret large volumes of visual data is improved by the integration of CNNs with IoT, providing creative solutions for automation and intelligent decision-making across numerous industries.

关键词： Training visualization Automation Computational modeling Surveillance Training data Feature extraction Convolutional neural networks Internet of Things image classification

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A Learning-based Approach for Martian image Compression

A Learning-based Approach for Martian Image Compression

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IEEE visual communications and image processing (VCIP)

作者： Qing Ding Mai Xu Shengxi Li Xin Deng Qiu Shen Xin Zou Beihang University Beijing China Nanjing University Nanjing China Beijing Institute of Spacecraft System Engineering Beijing China

ISBN: (纸本)9781665475938

For the scientific exploration and research on Mars, it is an indispensable step to transmit high-quality Martian images from distant Mars to Earth. image compression is the key technique given the extremely limited Mars-Earth bandwidth. Recently, deep learning has demonstrated remarkable performance in natural image compression, which provides a possibility for efficient Martian image compression. However, deep learning usually requires large training data. In this paper, we establish the first large-scale high-resolution Martian image compression (MIC) dataset. Through analyzing this dataset, we observe an important non-local self-similarity prior for Marian images. Benefiting from this prior, we propose a deep Martian image compression network with the non-local block to explore both local and non-local dependencies among Martian image patches. Experimental results verify the effectiveness of the proposed network in Martian image compression, which outperforms both the deep learning based compression methods and HEVC codec.

关键词： Deep learning Earth Microwave integrated circuits Mars image coding Codecs visual communication

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Relevant Tag Extraction Based on image visual Content 1st

Relevant Tag Extraction Based on Image Visual Content

引用

1st International conference on Applied Intelligence, ICAI 2023

作者： Fazal, Nancy Fränti, Pasi School of Computing University of Eastern Finland Joensuu Finland

ISBN: (纸本)9789819708260

Social media web services like Flickr allow users to share and freely annotate images with textual tags. These tags play a crucial part for text-based social image retrieval and browsing tasks. However, these tags are usually irrelevant and incomplete which limits their effectiveness and use. One fundamental problem is to interpret the relevance of tags with respect to the image visual content. Existing solutions have targeted either image visual content or user contributed tags separately to address the issue. Our proposed method Tag-tag, however, combines both aspects and exploit their semantic relationship. We use state of the art pretrained machine learning models in Tensorflow for object detection and NLP (Natural Language processing) for semantic analysis. Our experiments on a dataset of 219 randomly collected Flickr images demonstrates the applicability of our method. images with missing tags were excluded from the experiments. We identified two reasons where relevant tag was not found as: (1) relevant tag itself was missing from user contributed tag list, (2) relevant tag got skipped because it was either not found in the Wordnet dictionary or has no pretrained vector in word2vec-google-news-300 model. We identify this as one limitation of the proposed method. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Object detection

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SEPL-Net: A Semantics-Enhanced Pseudo Labeling Network for Semi-Supervised image Analysis

SEPL-Net: A Semantics-Enhanced Pseudo Labeling Network for S...

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2022 IEEE International conference on Multimedia and Expo, ICME 2022

作者： Xiao, Wenjing Hwang, Kai Chen, Min Li, Xianzhi School of Computer Science and Technology Huazhong University of Science and Technology China School of Data Science Chinese University of Hong Kong Hong Kong The Shenzhen Institute of Artificial Intelligence and Robotics for Society China

ISBN: (数字)9781665485630

ISBN: (纸本)9781665485630

As the mainstream solution for semi-supervised learning (SSL), pseudo-labeling-based approaches have achieved re-markable success. However, an obvious drawback of existing methods is that the valuable semantic relationships among categories are often ignored, thus leading to suboptimal encoded embeddings. To address this, we present a novel Semantics-Enhanced Pseudo Labeling Network, called SEPL-Net, for image analysis in a semi-supervised manner. SEPL-Net explores the prior knowledge of visual similarity between different classes to improve the quality of pseudo label decision making. Particularly, we encode semantic labels combined with the one-hot label to jointly train our network by exploiting their disagreement. To alleviate the difficulty of labeling unlabeled images due to the introduction of semantic labels, we further design different classifiers with differentiated strong augmentation modes to enable cooperative pseudo labeling. Extensive experimental results show that our SEPL-Net outperforms existing SSL methods with the averaged 1.84% accuracy improvement on image classification task. Code is available at https://***/sweetvicky/***. © 2022 IEEE.

关键词： Embeddings

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A Completed Gaussian Extended Binary Pattern for Texture image Classification 23

A Completed Gaussian Extended Binary Pattern for Texture Ima...

引用

Proceedings of the 2nd International conference on Signal processing, Computer Networks and communications

作者： Xiaochun Xu Weizheng Lin Dingrong Chen Nanling Su Leinuo Tang Changxu Cai College of Computer and Control Engineering Minjiang University China International Digital Economy College Minjiang University China

ISBN: (纸本)9798400716430

Texture image classification is a fundamental and challenging visual task and has wide range of applications. Binary pattern methods play an important role in texture feature extraction due to its ease of implementation and promising performance. To extract completed texture feature representation and improve the classification performance, this paper proposes a completed gaussian extend binary pattern for texture image classification. First, instead of the original pixel value, this paper uses the mean of local range to encode the binary pattern. Second, this paper introduces a novel gaussian sign pattern to fully represent the macro texture structure. Third, to achieve completed texture feature description, this paper presents a completed gaussian extended binary pattern, which combines the novel gaussian sign pattern, the local sign and magnitude pattern extracted from mean-processing texture image. To validate the effectiveness of the proposed completed gaussian extend binary pattern, experimental evaluations are conducted on three test subsets from Outex database. The evaluation results show that the proposed completed gaussian extended binary pattern achieves the state-pf-the-art classification performance.

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