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PatchSVD: A Non-Uniform SVD-Based Image Compression Algorithm PatchSVD：基于非均匀 SVD 的图像压缩算法
International Conference on Pattern Recognition Applications and Methods Pub Date : 2024-06-07 DOI: 10.5220/0012488500003654
Zahra Golpayegani, Nizar Bouguila
{"title":"PatchSVD: A Non-Uniform SVD-Based Image Compression Algorithm","authors":"Zahra Golpayegani, Nizar Bouguila","doi":"10.5220/0012488500003654","DOIUrl":"https://doi.org/10.5220/0012488500003654","url":null,"abstract":"Storing data is particularly a challenge when dealing with image data which often involves large file sizes due to the high resolution and complexity of images. Efficient image compression algorithms are crucial to better manage data storage costs. In this paper, we propose a novel region-based lossy image compression technique, called PatchSVD, based on the Singular Value Decomposition (SVD) algorithm. We show through experiments that PatchSVD outperforms SVD-based image compression with respect to three popular image compression metrics. Moreover, we compare PatchSVD compression artifacts with those of Joint Photographic Experts Group (JPEG) and SVD-based image compression and illustrate some cases where PatchSVD compression artifacts are preferable compared to JPEG and SVD artifacts.","PeriodicalId":410036,"journal":{"name":"International Conference on Pattern Recognition Applications and Methods","volume":" 11","pages":"886-893"},"PeriodicalIF":0.0,"publicationDate":"2024-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141371207","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
On Spectrogram Analysis in a Multiple Classifier Fusion Framework for Power Grid Classification Using Electric Network Frequency 论利用电网频率进行电网分类的多分类器融合框架中的谱图分析
International Conference on Pattern Recognition Applications and Methods Pub Date : 2024-03-27 DOI: 10.5220/0012418400003654
Georgios Tzolopoulos, Christos Korgialas, C. Kotropoulos
{"title":"On Spectrogram Analysis in a Multiple Classifier Fusion Framework for Power Grid Classification Using Electric Network Frequency","authors":"Georgios Tzolopoulos, Christos Korgialas, C. Kotropoulos","doi":"10.5220/0012418400003654","DOIUrl":"https://doi.org/10.5220/0012418400003654","url":null,"abstract":"The Electric Network Frequency (ENF) serves as a unique signature inherent to power distribution systems. Here, a novel approach for power grid classification is developed, leveraging ENF. Spectrograms are generated from audio and power recordings across different grids, revealing distinctive ENF patterns that aid in grid classification through a fusion of classifiers. Four traditional machine learning classifiers plus a Convolutional Neural Network (CNN), optimized using Neural Architecture Search, are developed for One-vs-All classification. This process generates numerous predictions per sample, which are then compiled and used to train a shallow multi-label neural network specifically designed to model the fusion process, ultimately leading to the conclusive class prediction for each sample. Experimental findings reveal that both validation and testing accuracy outperform those of current state-of-the-art classifiers, underlining the effectiveness and robustness of the proposed methodology.","PeriodicalId":410036,"journal":{"name":"International Conference on Pattern Recognition Applications and Methods","volume":"5 9","pages":"91-99"},"PeriodicalIF":0.0,"publicationDate":"2024-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140375871","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Semantic Properties of cosine based bias scores for word embeddings 基于余弦的词嵌入偏差分数的语义特性
International Conference on Pattern Recognition Applications and Methods Pub Date : 2024-01-27 DOI: 10.48550/arXiv.2401.15499
Sarah Schröder, Alexander Schulz, Fabian Hinder, Barbara Hammer
{"title":"Semantic Properties of cosine based bias scores for word embeddings","authors":"Sarah Schröder, Alexander Schulz, Fabian Hinder, Barbara Hammer","doi":"10.48550/arXiv.2401.15499","DOIUrl":"https://doi.org/10.48550/arXiv.2401.15499","url":null,"abstract":"Plenty of works have brought social biases in language models to attention and proposed methods to detect such biases. As a result, the literature contains a great deal of different bias tests and scores, each introduced with the premise to uncover yet more biases that other scores fail to detect. What severely lacks in the literature, however, are comparative studies that analyse such bias scores and help researchers to understand the benefits or limitations of the existing methods. In this work, we aim to close this gap for cosine based bias scores. By building on a geometric definition of bias, we propose requirements for bias scores to be considered meaningful for quantifying biases. Furthermore, we formally analyze cosine based scores from the literature with regard to these requirements. We underline these findings with experiments to show that the bias scores' limitations have an impact in the application case.","PeriodicalId":410036,"journal":{"name":"International Conference on Pattern Recognition Applications and Methods","volume":"73 11","pages":"160-168"},"PeriodicalIF":0.0,"publicationDate":"2024-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140491662","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Double Trouble? Impact and Detection of Duplicates in Face Image Datasets 双重麻烦？人脸图像数据集中重复图像的影响与检测
International Conference on Pattern Recognition Applications and Methods Pub Date : 2024-01-25 DOI: 10.5220/0012422500003654
Torsten Schlett, C. Rathgeb, Juan E. Tapia, Christoph Busch
{"title":"Double Trouble? Impact and Detection of Duplicates in Face Image Datasets","authors":"Torsten Schlett, C. Rathgeb, Juan E. Tapia, Christoph Busch","doi":"10.5220/0012422500003654","DOIUrl":"https://doi.org/10.5220/0012422500003654","url":null,"abstract":"Various face image datasets intended for facial biometrics research were created via web-scraping, i.e. the collection of images publicly available on the internet. This work presents an approach to detect both exactly and nearly identical face image duplicates, using file and image hashes. The approach is extended through the use of face image preprocessing. Additional steps based on face recognition and face image quality assessment models reduce false positives, and facilitate the deduplication of the face images both for intra- and inter-subject duplicate sets. The presented approach is applied to five datasets, namely LFW, TinyFace, Adience, CASIA-WebFace, and C-MS-Celeb (a cleaned MS-Celeb-1M variant). Duplicates are detected within every dataset, with hundreds to hundreds of thousands of duplicates for all except LFW. Face recognition and quality assessment experiments indicate a minor impact on the results through the duplicate removal. The final deduplication data is publicly available.","PeriodicalId":410036,"journal":{"name":"International Conference on Pattern Recognition Applications and Methods","volume":"68 12","pages":"801-808"},"PeriodicalIF":0.0,"publicationDate":"2024-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140496716","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Detecting Brain Tumors through Multimodal Neural Networks 通过多模态神经网络检测脑肿瘤
International Conference on Pattern Recognition Applications and Methods Pub Date : 2024-01-10 DOI: 10.5220/0012608600003654
Antonio Curci, Andrea Esposito
{"title":"Detecting Brain Tumors through Multimodal Neural Networks","authors":"Antonio Curci, Andrea Esposito","doi":"10.5220/0012608600003654","DOIUrl":"https://doi.org/10.5220/0012608600003654","url":null,"abstract":"Tumors can manifest in various forms and in different areas of the human body. Brain tumors are specifically hard to diagnose and treat because of the complexity of the organ in which they develop. Detecting them in time can lower the chances of death and facilitate the therapy process for patients. The use of Artificial Intelligence (AI) and, more specifically, deep learning, has the potential to significantly reduce costs in terms of time and resources for the discovery and identification of tumors from images obtained through imaging techniques. This research work aims to assess the performance of a multimodal model for the classification of Magnetic Resonance Imaging (MRI) scans processed as grayscale images. The results are promising, and in line with similar works, as the model reaches an accuracy of around 98%. We also highlight the need for explainability and transparency to ensure human control and safety.","PeriodicalId":410036,"journal":{"name":"International Conference on Pattern Recognition Applications and Methods","volume":"5 4","pages":"995-1000"},"PeriodicalIF":0.0,"publicationDate":"2024-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140511607","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Learning Independently from Causality in Multi-Agent Environments 基于因果关系的多智能体环境独立学习
International Conference on Pattern Recognition Applications and Methods Pub Date : 2023-11-05 DOI: 10.5220/0011747900003411
Rafael Pina, V. D. Silva, Corentin Artaud
{"title":"Learning Independently from Causality in Multi-Agent Environments","authors":"Rafael Pina, V. D. Silva, Corentin Artaud","doi":"10.5220/0011747900003411","DOIUrl":"https://doi.org/10.5220/0011747900003411","url":null,"abstract":"Multi-Agent Reinforcement Learning (MARL) comprises an area of growing interest in the field of machine learning. Despite notable advances, there are still problems that require investigation. The lazy agent pathology is a famous problem in MARL that denotes the event when some of the agents in a MARL team do not contribute to the common goal, letting the teammates do all the work. In this work, we aim to investigate this problem from a causality-based perspective. We intend to create the bridge between the fields of MARL and causality and argue about the usefulness of this link. We study a fully decentralised MARL setup where agents need to learn cooperation strategies and show that there is a causal relation between individual observations and the team reward. The experiments carried show how this relation can be used to improve independent agents in MARL, resulting not only on better performances as a team but also on the rise of more intelligent behaviours on individual agents.","PeriodicalId":410036,"journal":{"name":"International Conference on Pattern Recognition Applications and Methods","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130384231","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Rethinking Image-based Table Recognition Using Weakly Supervised Methods 基于图像的弱监督表识别方法的再思考
International Conference on Pattern Recognition Applications and Methods Pub Date : 2023-03-14 DOI: 10.5220/0011682600003411
N. Ly, A. Takasu, Phuc Nguyen, H. Takeda
{"title":"Rethinking Image-based Table Recognition Using Weakly Supervised Methods","authors":"N. Ly, A. Takasu, Phuc Nguyen, H. Takeda","doi":"10.5220/0011682600003411","DOIUrl":"https://doi.org/10.5220/0011682600003411","url":null,"abstract":"Most of the previous methods for table recognition rely on training datasets containing many richly annotated table images. Detailed table image annotation, e.g., cell or text bounding box annotation, however, is costly and often subjective. In this paper, we propose a weakly supervised model named WSTabNet for table recognition that relies only on HTML (or LaTeX) code-level annotations of table images. The proposed model consists of three main parts: an encoder for feature extraction, a structure decoder for generating table structure, and a cell decoder for predicting the content of each cell in the table. Our system is trained end-to-end by stochastic gradient descent algorithms, requiring only table images and their ground-truth HTML (or LaTeX) representations. To facilitate table recognition with deep learning, we create and release WikiTableSet, the largest publicly available image-based table recognition dataset built from Wikipedia. WikiTableSet contains nearly 4 million English table images, 590K Japanese table images, and 640k French table images with corresponding HTML representation and cell bounding boxes. The extensive experiments on WikiTableSet and two large-scale datasets: FinTabNet and PubTabNet demonstrate that the proposed weakly supervised model achieves better, or similar accuracies compared to the state-of-the-art models on all benchmark datasets.","PeriodicalId":410036,"journal":{"name":"International Conference on Pattern Recognition Applications and Methods","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114511425","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
Instance Segmentation Based Graph Extraction for Handwritten Circuit Diagram Images 基于实例分割的手写电路图图像提取
International Conference on Pattern Recognition Applications and Methods Pub Date : 2023-01-09 DOI: 10.48550/arXiv.2301.03155
Johannes Bayer, Amit Kumar Roy, A. Dengel
{"title":"Instance Segmentation Based Graph Extraction for Handwritten Circuit Diagram Images","authors":"Johannes Bayer, Amit Kumar Roy, A. Dengel","doi":"10.48550/arXiv.2301.03155","DOIUrl":"https://doi.org/10.48550/arXiv.2301.03155","url":null,"abstract":"Handwritten circuit diagrams from educational scenarios or historic sources usually exist on analogue media. For deriving their functional principles or flaws automatically, they need to be digitized, extracting their electrical graph. Recently, the base technologies for automated pipelines facilitating this process shifted from computer vision to machine learning. This paper describes an approach for extracting both the electrical components (including their terminals and describing texts) as well their interconnections (including junctions and wire hops) by the means of instance segmentation and keypoint extraction. Consequently, the resulting graph extraction process consists of a simple two-step process of model inference and trivial geometric keypoint matching. The dataset itself, its preparation, model training and post-processing are described and publicly available.","PeriodicalId":410036,"journal":{"name":"International Conference on Pattern Recognition Applications and Methods","volume":"79 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116011458","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Machine Fault Classification using Hamiltonian Neural Networks 基于哈密顿神经网络的机器故障分类
International Conference on Pattern Recognition Applications and Methods Pub Date : 2023-01-04 DOI: 10.48550/arXiv.2301.02243
Jer-Sheng Shen, Jawad Chowdhury, Sourav Banerjee, G. Terejanu
{"title":"Machine Fault Classification using Hamiltonian Neural Networks","authors":"Jer-Sheng Shen, Jawad Chowdhury, Sourav Banerjee, G. Terejanu","doi":"10.48550/arXiv.2301.02243","DOIUrl":"https://doi.org/10.48550/arXiv.2301.02243","url":null,"abstract":"A new approach is introduced to classify faults in rotating machinery based on the total energy signature estimated from sensor measurements. The overall goal is to go beyond using black-box models and incorporate additional physical constraints that govern the behavior of mechanical systems. Observational data is used to train Hamiltonian neural networks that describe the conserved energy of the system for normal and various abnormal regimes. The estimated total energy function, in the form of the weights of the Hamiltonian neural network, serves as the new feature vector to discriminate between the faults using off-the-shelf classification models. The experimental results are obtained using the MaFaulDa database, where the proposed model yields a promising area under the curve (AUC) of $0.78$ for the binary classification (normal vs abnormal) and $0.84$ for the multi-class problem (normal, and $5$ different abnormal regimes).","PeriodicalId":410036,"journal":{"name":"International Conference on Pattern Recognition Applications and Methods","volume":"87 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132710041","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 2
Evaluation of Induced Expert Knowledge in Causal Structure Learning by NOTEARS NOTEARS对因果结构学习中诱导专家知识的评价
International Conference on Pattern Recognition Applications and Methods Pub Date : 2023-01-04 DOI: 10.48550/arXiv.2301.01817
Jawad Chowdhury, Rezaur Rashid, G. Terejanu
{"title":"Evaluation of Induced Expert Knowledge in Causal Structure Learning by NOTEARS","authors":"Jawad Chowdhury, Rezaur Rashid, G. Terejanu","doi":"10.48550/arXiv.2301.01817","DOIUrl":"https://doi.org/10.48550/arXiv.2301.01817","url":null,"abstract":"Causal modeling provides us with powerful counterfactual reasoning and interventional mechanism to generate predictions and reason under various what-if scenarios. However, causal discovery using observation data remains a nontrivial task due to unobserved confounding factors, finite sampling, and changes in the data distribution. These can lead to spurious cause-effect relationships. To mitigate these challenges in practice, researchers augment causal learning with known causal relations. The goal of the paper is to study the impact of expert knowledge on causal relations in the form of additional constraints used in the formulation of the nonparametric NOTEARS. We provide a comprehensive set of comparative analyses of biasing the model using different types of knowledge. We found that (i) knowledge that corrects the mistakes of the NOTEARS model can lead to statistically significant improvements, (ii) constraints on active edges have a larger positive impact on causal discovery than inactive edges, and surprisingly, (iii) the induced knowledge does not correct on average more incorrect active and/or inactive edges than expected. We also demonstrate the behavior of the model and the effectiveness of domain knowledge on a real-world dataset.","PeriodicalId":410036,"journal":{"name":"International Conference on Pattern Recognition Applications and Methods","volume":"322 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133906041","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 3
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