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检索条件"任意字段=2021 10th International Conference on Computing and Pattern Recognition"

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Oracle Bone Inscription Image Retrieval Based on Improved ResNet Network 27th

Oracle Bone Inscription Image Retrieval Based on Improved Re...

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27th international conference on pattern recognition, ICPR 2024

作者： Ding, Jun Wang, Jiaoyan Aysa, Alimjan Xu, Xuebin Ubul, Kurban School of Computer Science and Technology Xinjiang University Ürümqi830046 China Xinjiang Multilingual Information Technology Key Laboratory Xinjiang University Ürümqi830046 China Joint International Research Laboratory of Silk Road Multilingual Cognitive Computing Xinjiang University Urumqi830046 China

ISBN: (纸本)9783031783043

Research on oracle bone inscription image retrieval is important for applications in academic and cultural heritage areas. the current oracle bone dataset faces problems such as the low similarity between the same category, the high similarity between the different categories, and imbalanced sample distribution. In addition, due to the complex background of oracle bone images, existing network models have certain limitations in extracting image features. To address these challenges, this study first adopts a Siamese network-based image retrieval method to learn feature representations of similar and dissimilar images. Subsequently, the existing dataset was partitioned, providing a practical and usable retrieval dataset for the oracle bone image retrieval field. Finally, an improved network model based on ResNet is proposed and integrated into the Siamese network framework. the model achieves the highest retrieval MP and MAP values of 83.26% and 90.68%, respectively, which is better than the current research. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Content based retrieval

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Can LLMs Perform Structured Graph Reasoning Tasks? 27th

Can LLMs Perform Structured Graph Reasoning Tasks?

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27th international conference on pattern recognition, ICPR 2024

作者： Agrawal, Palaash Vasania, Shavak Tan, Cheston Center for Frontier AI Research Agency for Science Technology and Research Singapore Singapore Institute of High Performance Computing Agency for Science Technology and Research Singapore Singapore UWCSEA Dover High School Singapore Singapore

ISBN: (纸本)9783031781711

Pretrained Large Language Models (LLMs) have demonstrated various reasoning capabilities through language-based prompts alone, particularly in unstructured task settings (i.e. tasks purely based on language semantics). However, LLMs often struggle with structured tasks, because of the inherent incompatibility of input representation. Reducing structured tasks to uni-dimensional language semantics often renders the problem trivial. Keeping the trade-off between LLM compatibility and structure complexity in mind, we design various graph reasoning tasks as a proxy to semi-structured tasks in this paper, in order to test LLMs’ ability to use representations beyond plain text. In particularly, we design 10 distinct problems of graph traversal, each representing increasing levels of complexity, and benchmark 5 different instruct-finetuned LLMs (GPT-4, GPT-3.5, Claude-2, Llama-2 and Palm-2) on the aforementioned tasks. Further, we analyse the performance of models across various settings such as varying sizes of graphs, as well as different forms of k-shot prompting. We highlight various limitations, biases and properties of LLMs through this benchmarking process, such as an inverse relation to the average degrees of freedom of traversal per node in graphs, the overall negative impact of k-shot prompting on graph reasoning tasks, and a positive response bias which prevents LLMs from identifying the absence of a valid solution. Finally, we introduce a new prompting technique specially designed for graph traversal tasks (PathCompare), which demonstrates a notable increase in the performance of LLMs in comparison to standard prompting techniques such as Chain-of-thought (CoT) (the code for reproducing the results of this paper can be found at https://***/PalaashAgrawal/LLMGraphTraversal). © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Semantics

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EDM10: A Polyphonic Stereo Dataset with Identical BGM for Musical Instrument Identification 27th

EDM10: A Polyphonic Stereo Dataset with Identical BGM for ...

引用

27th international conference on pattern recognition, ICPR 2024

作者： Mukherjee, Himadri Marciano, Matteo Dhar, Ankita Roy, Kaushik TISA Lab Department of Computer Science West Bengal State University Barasat India Gazelien Records Lab Department of Arts and Humanities Music Program New York University Abu Dhabi Abu Dhabi United Arab Emirates Department of Computer Science and Engineering Sister Nivedita University New Town India

ISBN: (纸本)9783031784972

Music Signal Processing has significantly evolved in the past decades. One of the major areas of interest in this field has been automatic music transcription. It is a challenging task by itself that aggravates even more when the input audio is polyphonic (multiple instruments and timbres are played simultaneously). this requires the identification of musical instruments in the piece at the outset. the field of music signal processing that deals with this aspect is known as automatic music instrument identification. this field also has the potential of categorizing and recommending music based on instruments. Disparate datasets have been proposed to date for this task but none of them have interclass background similarity to the best of our knowledge. Further, the lead melody being played also varies from class to class in most cases. these aspects can introduce a possible bias for the machine learning models that can get manipulated unfairly by the additional variances in the classes other than the lead instrument itself. this sets the stage for a dataset where the classes are different only in terms of the tone of the lead instrument alone. In this paper, we introduce the first musical instrument dataset of 10 musical instruments with Electronic Dance Music melodies (EDM10) having identical background music (BGM) across instruments. this dataset is the first of its kind wherein synthetic tones have been used that have taken over the Globe. We introduce out-of-mood testing using exotic scales for musical instrument identification. the dataset is composed of 35800 polyphonic clips of 3 s each and a baseline result of 89.73% was obtained using a deep learning-based approach. the dataset is freely available for research purposes. https://***/yV5e36TK1jHMKjpC6. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Electronic musical instruments

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ICPR 2024 Competition on Beyond Visible Spectrum: AI for Agriculture 27th

ICPR 2024 Competition on Beyond Visible Spectrum: AI for A...

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27th international conference on pattern recognition, ICPR 2024

作者： Han, Liangxiu Huang, Wenjiang Zhang, Xin Dong, Yingying Sobeih, Tam Lin, Yufan Department of Computing and Mathematics Manchester Metropolitan University ManchesterM15 6BH United Kingdom Key laboratory of Digital Earth Science Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China Institute of Data Science National Cheng Kung University Taiwan70101 China

ISBN: (纸本)9783031801389

the ICPR 2024 Competition on "Beyond Visible Spectrum: AI for Agriculture" presents an exciting opportunity for researchers and practitioners to advance computer vision techniques in agricultural crop disease monitoring. By leveraging the vast multi/hyper spectral remote sensing datasets, participants are encouraged to develop innovative machines/deep learning algorithms. the primary objective is to improve the accuracy and efficiency of crop disease identification, thereby fostering sustainable agricultural practices and facilitating global knowledge generation for enhanced food security. this challenge presents participants with the opportunity to make significant contributions to the advancement of precision farming and crop monitoring techniques. By improving the machines/deep learning models, the competition aims to drive forward the capabilities of agricultural technology, making it more efficient, accurate, and sustainable. A total of 190 people from more than 10 countries in Europe, Asia and North America entered the competition by the deadline date, with a total of 126 submissions of results. the rank1 team obtained an accuracy of 73.3% in crop disease classification task with hyperspectral dataset. In the self-supervised learning task, the rank1 team obtained 79.2% accuracy. We report the leader-boards of the competition in this report and present three innovative approaches of the participants. the ICPR 2024 Competition on "Beyond Visible Spectrum" is not just a competition;it is a call to action for the computer research community to bring about transformative changes in how agricultural data is analyze and utilized, paving the way for smarter, more sustainable farming practices that can benefit communities worldwide. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Self-supervised learning

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Empowering Healthcare with Deep Learning: An Application for Early Detection of Skin Cancer 4th

Empowering Healthcare with Deep Learning: An Application fo...

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4th international conference on Intelligent Systems and pattern recognition, ISPR 2024

作者： Oluwatobi, Komolafe Joseph Asghar, Ikram Diyan, Muhammad Nawaz, Rab Saleem, Sidra Ullah, Rahmat Ahmad, Jawad School of Computing Engineering and Digital Technologies Teesside University Middlesbrough United Kingdom University of Agriculture Faisalabad Pakistan School of Computer Science and Electronic Engineering University of Essex Colchester United Kingdom Edinburgh Napier University Edinburgh United Kingdom

ISBN: (纸本)9783031821523

Skin cancer is a hazardous ailment and a leading contributor to mortality. Early diagnosis of skin cancer can significantly minimize or prevent these fatalities. the diagnostic process can be both lengthy and costly. this study aims to establish a basis for creating an exact and effective convolutional neural network (CNN) model for detecting skin cancer. this model aims to enhance the early and accurate diagnosis of skin cancers, potentially leading to a decrease in the number of associated fatalities. the model utilizes a convolutional neural network with Keras Tensorflow as the backend to categorize seven distinct categories of skin cancer. Subsequently, the results are analyzed to determine the practical applications of the model. the MobileNet optimizer is used for classification. A web application has been developed that offers dermatologists the three most likely diagnostics for a specific blister. It will aid in swiftly recognizing patients with high priorities and accelerating their workflow. the application generates a result within a time frame of 5–10 seconds. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Lung cancer

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Methodology of the Construction of a GDPLL(k) Grammar-Based Syntactic pattern recognition System 10th

Methodology of the Construction of a GDPLL(<i>k</i>) Grammar...

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10th international conference on Computer recognition Systems (CORES)

作者： Flasinski, Mariusz Jurek, Janusz Jagiellonian Univ Informat Technol Syst Dept Ul Prof St Lojasiewicza 4 PL-30348 Krakow Poland

ISBN: (纸本)9783319591629

GDPLL(k) grammars have been introduced as a tool for the construction of syntactic pattern recognition-based systems. the grammars have been successfully used in several different applications. the practical experience with the implementation of a syntactic pattern recognition system based on GDPLL(k) grammars has served to define methodological guidelines for constructing such systems. In the paper key methodological issues are presented.

关键词： Syntactics

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CitiSafe: Adaptive Spatial pattern Knowledge Using Fp-growth Algorithm for Crime Situation recognition

CitiSafe: Adaptive Spatial Pattern Knowledge Using Fp-growth...

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IEEE 10th international conference on Ubiquitous Intelligence and computing (UIC) / IEEE 10th international conference on Autonomic and Trusted computing (ATC)

作者： Isafiadel, Omowunmi E. Bagula, Antoine B. Univ Cape Town Dept Comp Sci ISAT Lab ZA-7925 Cape Town South Africa

ISBN: (纸本)9781479924813

Crime prevention and control are issues of great concern to the government and public safety agencies. If these issues are not well controlled and managed, they could drastically affect the economy of a country negatively over time, since more emigration is naturally induced. With the existence of crime databases in the society, data mining techniques are viable tools for crime situation recognition and control. However, most existing techniques often fall short to consider salient features during the mining process. In this paper, we experimented on various crime categories across 32 locations within the western cape province of the republic of South Africa. Our research focus is to create a flexible and effective, yet simple, solution to crime situation recognition. A successful mining mechanism, adopting a batch-merge paradigm, that builds upon the Fp-Growth algorithm, referred to as CitiSafe algorithm is presented, followed by concluding remarks. the visualization techniques presenting the crime patterns, through our algorithm, would assist law enforcement agencies and public safety organisations to channelize their resources accordingly to achieve a focused and effective crime prevention strategy.

关键词： Public safety Data Mining Crime Situation recognition and Intelligent Platforms

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Indextron 10th

Indextron

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10th international conference on pattern recognition Applications and Methods (ICPRAM)

作者： Mikhailov, Alexei Karavay, Mikhail Russian Acad Sci Inst Control Problems Profsoyuznaya St 65 Moscow Russia

ISBN: (纸本)9789897584862

How to do pattern recognition without artificial neural networks, Bayesian classifiers, vector support machines and other mechanisms that are widely used for machine learning? the problem with pattern recognition machines is time and energy demanding training because lots of coefficients need to be worked out. the paper introduces an indexing model that performs training by memorizing inverse patterns mostly avoiding any calculations. the computational experiments indicate the potential of the indexing model for artificial intelligence applications and, possibly, its relevance to neurobiological studies as well.

关键词： pattern recognition Machine Learning Neural Networks Inverse Sets Inverse patterns Multidimensional Indexing

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Multivariate Analysis of Text Data from Image Information Acquired by Wearable Device 10

Multivariate Analysis of Text Data from Image Information Ac...

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Joint 10th international conference on Soft computing and Intelligent Systems (SCIS) / 19th international Symposium on Advanced Intelligent Systems (ISIS)

作者： Domoto, Eri Shibata, Junko Fukushima, Mizuki Okuhara, Koji Hiroshima Univ Econ Fac Econ Hiroshima Japan Kobe Gakuin Univ Fac Econ Kobe Hyogo Japan Fujitsu Hokuriku Syst Ltd Kanazawa Ishikawa Japan Toyama Prefectural Univ Grad Sch Engn Toyama Japan

ISBN: (纸本)9781538626337

In this study, we obtain life log data from visibility information using smart glass which is a wearable device. In the proposed system, it consists of a text data collection part by cooperation between smart glass and image recognition API and activity recognition part. the activity recognition part incorporates multidimensional scaling analysis method and correspondence analysis for classifying data. As a result of utilizing the development system in real life, it was able to detect similarities and events for behavior from the difference of the object caught on sight.

关键词： Multivariate Analysis Life Log Behavior pattern Text Data Mining

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recognition of Fuzzy or Incompletely Described Objects 10th

Recognition of Fuzzy or Incompletely Described Objects

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10th international conference on Computer recognition Systems (CORES)

作者： Kulikowski, Juliusz L. Polish Acad Sci Nalecz Inst Biocybernet & Biomed Engn 4 Ks Trojdena St PL-02109 Warsaw Poland

ISBN: (纸本)9783319591629

Typical pattern recognition problem consists in assigning of a given object (result of observation) to one of previously defined similarity classes of objects. the problem has an unique solution if the classes are disjoint;otherwise it may happen that the considered object can be assigned to a class only on a limited certainty level. A more general problem arises if the object being to be recognized has not been described with a full accuracy. the situations of uncertainty consisting in missing some components of objects description and in inaccuracy of some objects' features or parameters description are considered. An approach to the solution of the ill-described objects recognition based on the concepts of relative logic is proposed. this makes the proposed approach closer to a natural human decision making supported by intuition and, as such, useful in the case of uncertainty concerning the input data of the recognition problem.

关键词： pattern recognition Limited input data Input data uncertainty Information variables Relative logic Semi-ordering

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