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检索条件"任意字段=1st International Conference on Image Processing Machine Learning and Pattern Recognition"

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Catastrophic Forgetting in Continual Concept Bottleneck Models 21st

Catastrophic Forgetting in Continual Concept Bottleneck Mode...

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21st international conference on image Analysis and processing (ICIAP)

作者： Marconato, Emanuele Bontempo, Gianpaolo Teso, stefano Ficarra, Elisa Calderara, Simone Passerini, Andrea Univ Trento Trento Italy Univ Modena & Reggio Emilia Modena Italy Univ Pisa Pisa Italy

ISBN: (纸本)9783031133244;9783031133237

Almost all Deep learning models are dramatically affected by Catastrophic Forgetting when learning over continual streams of data. To mitigate this problem, several strategies for Continual learning have been proposed, even though the extent of the forgetting is still unclear. In this paper, we analyze Concept Bottleneck (CB) models in the Continual learning setting and we investigate the effect of high-level features supervision on Catastrophic Forgetting at the representation layer. Consequently, we introduce two different metrics to evaluate the loss of information on the learned concepts as new experiences are encountered. We also show that the obtained Saliency maps remain more stable with the attributes supervision. The code is available at https://***/Bontempogianpaolo1/continualExplain

关键词： Continual learning Explainable Artificial Intelligence Catastrophic Forgetting Concept Bottleneck models

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Genetic Algorithm for image recognition in Smart Systems Applications 1

Genetic Algorithm for Image Recognition in Smart Systems App...

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1st IEEE international Maghreb Meeting of the conference on Sciences and Techniques of Automatic Control and Computer Engineering, MI-stA 2021

作者： Jannoud, Ismael Masoud, Mohammad Jaradat, Yousef Zerek, Amer Al-Zaytoonah University of Jordan Faculty of Engineering and Technology Amman Jordan University of Zawia Dept. of Electrical and Electronic Engineering Tripolli Libya

ISBN: (纸本)9781665418560

image recognition in smart systems and internet of things applications is rapidly developing. Significant advances in mobile computing technology and machine learning are expanding horizons to use image recognition in smart systems and IoT applications. This paper use the smart system based image matching system utilizing genetic algorithm to design an integrated service to improve the management of image recognition. An efficient genetic algorithm used to match an unknown pattern which represents a partial image with original image. This technique uses a parameter, namely, chromosome. It transformed the spatial coordinates position into a sequence of bits of small length depending on the dimension of the original image, whereby the matching will be obtained in a short time. The system was tested on a sample images showed high matching rate (97%). © 2021 IEEE.

关键词： Chromosomes

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Enhancing Aadhar Card image Security with machine learning-Based Face Morphing Detection

Enhancing Aadhar Card Image Security with Machine Learning-B...

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Communications and Computer Science (InCCCS), international conference on

作者： Aziz Makandar Syeda Bibi Javeriya Department of Computer Science KSAWU Vijayapura India Department of Computer Science KSAWU Vijayapur

ISBN: (数字)9798350358858

ISBN: (纸本)9798350358865

In this study on digital image forensics, the focus is on detecting face morphing in Aadhar card images for reliable identity verification. The proposed approach integrates advanced image processing techniques, including Local Binary patterns (LBP), Histogram of Oriented Gradients (HOG), and Local Phase Quantization (LPQ), to extract detailed features from both authentic and morphed Aadhar card images. machine learning classifiers such as Support Vector machine (SVM), Random Forest (RF), and KNN (k-nearest neighbors) are employed to assess the effectiveness of the proposed methods. The research highlights the success of combining LBP, HOG, and LPQ in detecting face morphing and offering significant insights into the appropriateness of various classifiers in the realm of image forensics, as measured by metrics like accuracy, precision, recall, and f1-score.

关键词： Support vector machines Radio frequency Accuracy Biometrics (access control) Digital images Nearest neighbor methods Feature extraction

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A machine learning Approach for Classification of Lemon Leaf Diseases 1st

A Machine Learning Approach for Classification of Lemon Leaf...

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1st international conference on Computing, Communication and learning, CoCoLe 2022

作者： Sahu, Soumya Ranjan Jena, Sudarson Panda, Sucheta VSSUT Burla India SUIIT Sambalpur University Jyoti Vihar Burla India

ISBN: (纸本)9783031217494

Automated classification of plant leaf diseases is one of the complex concerns in robotics and machine learning fields. Several models have been introduced for detecting and classifying leaf diseases with high classification performance. With the advancement of image processing and machine learning, predicting diseases is the most significant research in recent years. This paper aims to classify lemon leaf diseases by segmenting the diseased part. The lemon leaf dataset is split into training and testing folders. The presented classification method consists of segmentation, feature extraction, and classification steps. For the classification of lemon leaf disease, at first, segmentation is done by using the K-mean clustering algorithm. The infected part of the lemon leaf can be partitioned by the above algorithm. The feature extraction is performed using Gray Level Co-occurrence Matrices (GLCM) method, to extract the texture features from the image. The GLCM method generates the statistical features from the image. The features are passed to the Support Vector machine (SVM) for the classification of lemon leaf disease. Experiments are conducted for the lemon leaf dataset by taking some samples and the results demonstrate high classification accuracy at a faster speed than other traditional classification methods. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Support vector machines

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learning Advisor Networks for Noisy image Classification 21st

Learning Advisor Networks for Noisy Image Classification

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21st international conference on image Analysis and processing (ICIAP)

作者： Ricci, Simone Uricchio, Tiberio Del Bimbo, Alberto Univ Florence Florence Italy

ISBN: (纸本)9783031064302;9783031064296

In this paper, we introduced the novel concept of advisor network to address the problem of noisy labels in image classification. Deep neural networks (DNN) are prone to performance reduction and overfitting problems on training data with noisy annotations. Weighting loss methods aim to mitigate the influence of noisy labels during the training, completely removing their contribution. This discarding process prevents DNNs from learning wrong associations between images and their correct labels but reduces the amount of data used, especially when most of the samples have noisy labels. Differently, our method weighs the feature extracted directly from the classifier without altering the loss value of each data. The advisor helps to focus only on some part of the information present in mislabeled examples, allowing the classifier to leverage that data as well. We trained it with a meta-learning strategy so that it can adapt throughout the training of the main model. We tested our method on CIFAR10 and CIFAR100 with synthetic noise, and on Clothing1M that contains real-world noise, reporting state-of-the-art results.

关键词： Meta learning Advisor network Noisy labels

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Wirelessly Controlled Plant Health Monitoring and Medicate System Based on IoT Technology 1st

Wirelessly Controlled Plant Health Monitoring and Medicate S...

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1st international conference on Advanced Network Technologies and Intelligent Computing (ANTIC)

作者： Ayalew, Lijaddis Getnet Mattihalli, Channamallikarjuna Asmare, Fanuel Melak Bahir Dar Univ Fac Elect & Comp Engn Bahir Dar Inst Technol Bahir Dar Ethiopia

ISBN: (纸本)9783030960407;9783030960391

The availability of good quality fruits and vegetables is paramount in preventing starvation and minimizing outbreaks of diseases which leads to improving quality of life. One of the major obstacles of the mentioned availability is plant leaf disease. Although manpower plays a vital role in detecting such problems it is time-intensive, expensive, and very inefficient. Thus, developing a mechanism to vigorously monitor leaf's health and detect diseases of plant leaves at early stages is mandatory so that one can produce plenty. In this contribution, a system that detects leaf disease is developed using image processing algorithms, the k-nearest neighbor (KNN), support vector machine (SVM), and multilayer perception (MLP) machine learning algorithms are compared based on plant disease detection and classification systems performances. We also developed a prototype of simple-to-install technology that can recognize leaf diseases and allow medicine flow based on the results. This paper presents a smart plant health monitoring system that takes into account humidity, temperature, and soil contents.

关键词： image processing IoT KNN MLP SVM

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learning Systems in Forgery image Detection 1st

Learning Systems in Forgery Image Detection

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1st international conference on Intelligent Systems in Computing and Communications, ISCComm 2023

作者： Shetty, Sudheer Ganesh, Devadiga Likhit kumar Poojary, Rahul R. Raviraj Tejas, R. Hegde, Nagaratna P. Vikkurty, Sireesha Department of ISE Sahyadri College of Engineering & Management Mangaluru Affiliated to Visvesvaraya Technological University Karnataka Belagavi India Department of ISE Alvas Institute of Engineering and Technology Moodbidri Affiliated to Visvesvaraya Technological University Karnataka Belagavi India Department of CSE Vasavi College of Engineering Telangana Hyderabad India

ISBN: (纸本)9783031756078

In an era dominated by the ubiquitous sharing of visual content on social media, the proliferation of fake photographs and digital fakes has emerged as a critical issue. This paper explores how fake images are detected with fusion of by various techniques of image processing, CNN, and machine learning, presenting an innovative approach to address the intricate challenge of identifying manipulated images. This paper gives insight on various approaches of image processing techniques and state-of-the-art machine learning algorithms used in a robust forgery image detection system. A comprehensive framework is proposed on various image forensic methods, ensuring the precise detection of fake images across diverse contexts. The paper also emphasizes the impact of countering the dissemination of fake photographs to preserve the credibility of digital media. This combination of image processing, CNN and machine learning propels the ongoing quest to uncover the truth behind the surge of digital fakes in today's digital landscape. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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Reliable and Efficient image processing and Deep machine learning for large-scale Digital image Retrieval 1

Reliable and Efficient Image Processing and Deep Machine Lea...

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1st IEEE international Interdisciplinary Humanitarian conference for Sustainability, IIHC 2022

作者： Selvi, G Thamarai Hamdy, Amal Vijaya, K Sri Kumar, Nellore Manoj Pallavi, L. Rao, Kanusu Srinivasa Sri Sai Ram Institute of Technology Department of ECE Tamilnadu Chennai India College of Women Physics Department Ain Shams University Egypt P v P Siddhartha Institute of Technology Department of IT Andhra Pradesh Vijayawada India Chennai Thandalam India B v Raju Institute of Technology Department of CSE Telangana Narsapur India Yogi Vemana University Department of Computer Science and Technology Andhra Pradesh India

ISBN: (纸本)9781665456876

Retrieving relevant photos from a database by analysing their differences and similarities is an essential part of machine vision. Numerous applications exist in areas such as storage, object recognition, localisation, and recognition with limited data. In this study, we take a look at the issue of image retrieval, in which a group of pictures is retrieved from a huge database in order to find ones that are most like the one the user has submitted (the 'query image'). This thesis investigates the origins of the issue, as well as its present-day manifestations and potential solutions. To the best of our knowledge, however, there is currently no accessible generic text removal method that can be used to any font, script, language, or shape to remove all or user-specified text sections. The difficulties of multi-lingual and curved text identification and inpainting are carried over into the development of a general text eraser for real-world settings. To the best of our knowledge, however, there is currently no accessible generic text removal method that can be used to any font, script, language, or shape to remove all or user-specified text sections. Creating a general text eraser for use in real-world scenarios is difficult since it takes on the difficulties of both multi-lingual text detection and curved text inpainting. By adhering to these standards, our method significantly outperforms the state of the art on four benchmark datasets. This includes more complicated algorithms with auxiliary components. Furthermore, we offer a qualitative evaluation of our trained representation, which shows that despite its small size, it is able to gather information from highly localised and discriminative regions, much like an implicit attention mechanism. © 2022 IEEE.

关键词： Computer vision

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Optimal Feature Subset Selection For pattern Classification and recognition Using Spectral Entropy as a Complexity Measure From Alcohol and Sleep EEG Data

Optimal Feature Subset Selection For Pattern Classification ...

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Innovations in High Speed Communication and Signal processing (IHCSP), international conference on

作者： Takveer Singh Centre for Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

The quantity of unprocessed data in multi-channel EEG analysis is enormous. Most datasets have some level of noise or redundancy, which could lower the accuracy of any relevant features that are uncovered. This result in a rise in computational complexity and a corresponding rise in the importance of concerns related to memory consumption. Consequently, feature selection has become the matter of significant research in the arenas of machine learning and statistics since it is crucial for efficient analysis of data while needing less memory and time. Based on the findings of this study, selecting features with high class separability from multichannel EEG recordings is an efficient method for performing supervised learning tasks. With this study, we intend to test the following theory: The best feature subset will have features that are substantially linked with the class labels but not connected with each other. Using the same alcoholic EEG database as these other investigations, we created and compared three feature selection algorithms. The purpose here is to use class knowledge in a feature selection technique to enhance the accuracy with which classes may be detected.

关键词： Technological innovation Databases Sleep Memory management Supervised learning Signal processing algorithms Sensitivity and specificity

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international conference on Automation, Computing and Renewable Systems, ICACRS 2022 - Proceedings

International Conference on Automation, Computing and Renewa...

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1st IEEE international conference on Automation, Computing and Renewable Systems, ICACRS 2022

ISBN: (纸本)9781665460842

The proceedings contain 251 papers. The topics discussed include: optically controlled microwave sensor for biomedical applications;deep learning network for object detection under the poor lighting condition;a brief analysis on machine learning classifiers for intrusion detection to enhance network security;a brief review on melanoma diagnosis models using machine learning techniques;equilibrium optimizer with deep learning model for autism spectral disorder classification;automated intracranial hemorrhage detection and classification using rider optimization with deep learning model;heart disease prediction and classification using machine learning and transfer learning model;review of machine learning algorithms for autism spectrum disorder prediction;novel approach to non-invasive blood glucose monitoring based on visible laser light;a machine learning based approach for breast cancer prediction;covid-19 infection segmentation using deep learning techniques;retinal fundus image retrieval and classification using optimal deep learning model;breast cancer segmentation by k-means and classification by machine learning;an extensive review of machine learning techniques for EEG signal processing;hybrid machine learning based false data injection attack detection and mitigation model for waste water treatment plant;twitter sentiment analysis with machine learning;hybrid particle swarm optimization with deep learning driven sarcasm detection on social media;design of kernel extreme learning machine based intelligent crop yield prediction model;and reliable densely connected network with machine learning based diabetic retinopathy grading approach.

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