Relevant bibliographies by topics / Multi-scale classifications

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Multi-scale classifications'

Author: Grafiati
Published: 5 June 2025
Last updated: 15 July 2025

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Journal articles on the topic "Multi-scale classifications"

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Li, Zhi, Zhefeng Wang, Zhicheng Wei, et al. "Cross-Oilfield Reservoir Classification via Multi-Scale Sensor Knowledge Transfer." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 5 (2021): 4215–23. http://dx.doi.org/10.1609/aaai.v35i5.16545.

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Reservoir classification is an essential step for the exploration and production process in the oil and gas industry. An appropriate automatic reservoir classification will not only reduce the manual workloads of experts, but also help petroleum companies to make optimal decisions efficiently, which in turn will dramatically reduce the costs. Existing methods mainly focused on generating reservoir classification in a single geological block but failed to work well on a new oilfield block. Indeed, how to transfer the subsurface characteristics and make accurate reservoir classification across t
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Zhang, Lilun, Dezhi Wang, Changchun Bao, Yongxian Wang, and Kele Xu. "Large-Scale Whale-Call Classification by Transfer Learning on Multi-Scale Waveforms and Time-Frequency Features." Applied Sciences 9, no. 5 (2019): 1020. http://dx.doi.org/10.3390/app9051020.

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Whale vocal calls contain valuable information and abundant characteristics that are important for classification of whale sub-populations and related biological research. In this study, an effective data-driven approach based on pre-trained Convolutional Neural Networks (CNN) using multi-scale waveforms and time-frequency feature representations is developed in order to perform the classification of whale calls from a large open-source dataset recorded by sensors carried by whales. Specifically, the classification is carried out through a transfer learning approach by using pre-trained state-
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Van, Phuong Ngoc Truc, and Vinh Minh Le. "Visualization design for choropleths in multi-scale statistical mapping." Science and Technology Development Journal 19, no. 2 (2016): 51–58. http://dx.doi.org/10.32508/stdj.v19i2.666.

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Multi-scale maps are one of those which are produced and displayed on screens at different levels of scale. Therefore, multi-scale mapping needs new approachs and concepts. This article introduces principles of multi-scale choropleth maps. The principles are based on cartographic principles and the screen environment. The scale ranges are defined by readable smallest area units. There is a changeover to larger administrative units at a reduction of scale. Data classifications and color ramps (symbology) for different scale ranges satisfy traditional rules and be consistent throughout the range
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Ahmed, Soaad, Naira Elazab, Mostafa M. El-Gayar, Mohammed Elmogy, and Yasser M. Fouda. "Multi-Scale Vision Transformer with Optimized Feature Fusion for Mammographic Breast Cancer Classification." Diagnostics 15, no. 11 (2025): 1361. https://doi.org/10.3390/diagnostics15111361.

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Background: Breast cancer remains one of the leading causes of mortality among women worldwide, highlighting the critical need for accurate and efficient diagnostic methods. Methods: Traditional deep learning models often struggle with feature redundancy, suboptimal feature fusion, and inefficient selection of discriminative features, leading to limitations in classification performance. To address these challenges, we propose a new deep learning framework that leverages MAX-ViT for multi-scale feature extraction, ensuring robust and hierarchical representation learning. A gated attention fusi
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Zhou, Weixun, Yongxin Shi, and Xiao Huang. "Multi-View Scene Classification Based on Feature Integration and Evidence Decision Fusion." Remote Sensing 16, no. 5 (2024): 738. http://dx.doi.org/10.3390/rs16050738.

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Leveraging multi-view remote sensing images in scene classification tasks significantly enhances the precision of such classifications. This approach, however, poses challenges due to the simultaneous use of multi-view images, which often leads to a misalignment between the visual content and semantic labels, thus complicating the classification process. In addition, as the number of image viewpoints increases, the quality problem for remote sensing images further limits the effectiveness of multi-view image classification. Traditional scene classification methods predominantly employ SoftMax
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Lim, Ee Hui, and David Suter. "3D terrestrial LIDAR classifications with super-voxels and multi-scale Conditional Random Fields." Computer-Aided Design 41, no. 10 (2009): 701–10. http://dx.doi.org/10.1016/j.cad.2009.02.010.

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Du, Yingjie, and Ning Ding. "A Systematic Review of Multi-Scale Spatio-Temporal Crime Prediction Methods." ISPRS International Journal of Geo-Information 12, no. 6 (2023): 209. http://dx.doi.org/10.3390/ijgi12060209.

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Crime is always one of the most important social problems, and it poses a great threat to public security and people. Accurate crime prediction can help the government, police, and citizens to carry out effective crime prevention measures. In this paper, the research on crime prediction is systematically reviewed from a variety of temporal and spatial perspectives. We describe the current state of crime prediction research from four perspectives (prediction content, crime types, methods, and evaluation) and focus on the prediction methods. According to various temporal and spatial scales, temp
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Li, Dengao, Ye Tao, Jumin Zhao, and Hang Wu. "Classification of Congestive Heart Failure from ECG Segments with a Multi-Scale Residual Network." Symmetry 12, no. 12 (2020): 2019. http://dx.doi.org/10.3390/sym12122019.

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Congestive heart failure (CHF) poses a serious threat to human health. Once the diagnosis of CHF is established, clinical experts need to assess the severity of CHF in a timely manner. It is proved that electrocardiogram (ECG) signals are useful for assessing the severity of CHF. However, since the ECG perturbations are subtle, it is difficult for doctors to detect the differences of ECGs. In order to help doctors to make an accurate diagnosis, we proposed a novel multi-scale residual network (ResNet) to automatically classify CHF into four classifications according to the New York Heart Assoc
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Liu, Yanzhang, Jinqi Cai, and Guirong Tan. "Multi-Level Circulation Pattern Classification Based on the Transfer Learning CNN Network." Atmosphere 13, no. 11 (2022): 1861. http://dx.doi.org/10.3390/atmos13111861.

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Deep learning artificial intelligence technology, which has the advantages of nonlinear mapping ability, massive information extraction ability, spatial-temporal modeling ability, and so on, provides new ideas and methods for further improving the accuracy of weather and climate extreme event prediction. A transfer learning CNN (Convolutional Neural Networks) classification model is established to classify the circulation patterns, along with the newly reconstructed dataset of regional persistent historical heavy rain events, daily rainfall data of 2474 observational stations, and the NCEP/NCA
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Kalacska, Margaret, Oliver Lucanus, Leandro Sousa, and J. Pablo Arroyo-Mora. "A New Multi-Temporal Forest Cover Classification for the Xingu River Basin, Brazil." Data 4, no. 3 (2019): 114. http://dx.doi.org/10.3390/data4030114.

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We describe a new multi-temporal classification for forest/non-forest classes for a 1.3 million square kilometer area encompassing the Xingu River basin, Brazil. This region is well known for its exceptionally high biodiversity, especially in terms of the ichthyofauna, with approximately 600 known species, 10% of which are endemic to the river basin. Global and regional scale datasets do not adequately capture the rapidly changing land cover in this region. Accurate forest cover and forest cover change data are important for understanding the anthropogenic pressures on the aquatic ecosystems.
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Dissertations / Theses on the topic "Multi-scale classifications"

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Cai, Jinzhou. "Large-scale Multi-label Video Classification." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-235280.

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Deep learning has been established as a powerful method to tacklevideo classification tasks. Encouraged by the remarkable success, thisthesis propose several effective classification models rely on the largestvideo dataset YouTube-8M. First, we investigate the diversity of theDense Neural Nets (DNN) by exploring several factors which controlthe corresponding performance. Furthermore, inspired by the wideand deep classification model which was originally used in the recommendationsystem, we build up a new classification model whichmodify the wide and deep part separately. Despite that, the thes
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Jovanovich, Aleksandar. "Review of Large-Scale Coordinate Descent Algorithms for Multi-class Classification with Memory Constraints." Youngstown State University / OhioLINK, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=ysu1369860195.

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Mäenpää, T. (Topi). "The local binary pattern approach to texture analysis — extensions and applications." Doctoral thesis, University of Oulu, 2003. http://urn.fi/urn:isbn:9514270762.

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Abstract This thesis presents extensions to the local binary pattern (LBP) texture analysis operator. The operator is defined as a gray-scale invariant texture measure, derived from a general definition of texture in a local neighborhood. It is made invariant against the rotation of the image domain, and supplemented with a rotation invariant measure of local contrast. The LBP is proposed as a unifying texture model that describes the formation of a texture with micro-textons and their statistical placement rules. The basic LBP is extended to facilitate the analysis of textures with multiple
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Chemmi, Houria, Dominique Petit, Pierre Levitz, Jean-Pierre Korb, and Mathieu Bérard. "Impact of multi-scale moisture transport on durability of hardened cement pastes." Diffusion fundamentals 10 (2009) 4, S. 1-3, 2009. https://ul.qucosa.de/id/qucosa%3A14093.

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We report on proton NMR relaxation of hardened Grey CEM I paste with two controlled degree of relative humidity. The comparison between these two aged materials evidences that the moisture transport only occurs on the meso and capillary scales. The proton population distribution vs T1 follows power laws with a negative exponent in the nanopore-mesopore pore scale, and a positive exponent in the mesopore-capillary pore scale. This opposite exponent sign shows that the spatial hierarchical proton distribution behaves like a surface and volume distribution on short and large scales, respectively.
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Huxter, Eric. "An approach to the multi-element and multi-scale classification of the limestone pavement environment of Hutton Roof and Farleton Fell, Cumbria, UK." Thesis, Kingston University, 2004. http://eprints.kingston.ac.uk/20728/.

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Limestone Pavements are highly significant components of the physiographic and ecological landscapes of the UK. As relict glacial features they are subject to destruction by natural processes but also by human intervention. This thesis identifies the most effective methods to monitor such change at a variety of temporal and spatial scales, based on the Morecambe Bay pavements at Hutton Roof and Farleton Fell. The starting point for such a study is a methodology to define the baseline on which to base change detection and the key to this is the development of a. suitably detailed scene model. T
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Sahin, Kerem. "Automatic Multi-scale Segmentation Of High Spatial Resolution Satellite Images Using Watersheds." Master's thesis, METU, 2013. http://etd.lib.metu.edu.tr/upload/12615350/index.pdf.

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Useful information extraction from satellite images for the use of other higher level applications such as road network extraction and update, city planning etc. is a very important and active research area. It is seen that pixel-based techniques becomes insufficient for this task with increasing spatial resolution of satellite imaging sensors day by day. Therefore, the use of object-based techniques becomes indispensable and the segmentation method selection is very crucial for object-based techniques. In this thesis, various segmentation algorithms applied in remote sensing literature are pr
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Voicu, Iulian. "Analyse, caractérisation et classification de signaux foetaux." Phd thesis, Université François Rabelais - Tours, 2011. http://tel.archives-ouvertes.fr/tel-00907317.

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Cette thèse s'inscrit dans le domaine biomédical, à l'interface entre l'instrumentation et le traitement du signal. L'objectif de ce travail est d'obtenir, grâce à une mélange de différentes informations, un monitorage de l'activité du fœtus (rythme cardiaque et mouvements fœtaux) pour apprécier son état de bien-être ou de souffrance, et ceci aux différents stades de la grossesse. Actuellement, les paramètres qui caractérisent la souffrance fœtale, issus du rythme cardiaque et des mouvements fœtaux, sont évalués par le médecin et ils sont réunis dans le score de Manning. Deux inconvénients maj
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Zille, Heiner Verfasser], and Sanaz [Gutachter] [Mostaghim. "Large-scale multi-objective optimisation : new approaches and a classification of the state-of-the-art / Heiner Zille ; Gutachter: Sanaz Mostaghim." Magdeburg : Universitätsbibliothek Otto-von-Guericke-Universität, 2019. http://d-nb.info/1219966339/34.

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Zille, Heiner [Verfasser], and Sanaz [Gutachter] Mostaghim. "Large-scale multi-objective optimisation : new approaches and a classification of the state-of-the-art / Heiner Zille ; Gutachter: Sanaz Mostaghim." Magdeburg : Universitätsbibliothek Otto-von-Guericke-Universität, 2019. http://d-nb.info/1219966339/34.

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Hütter, Geralf. "Multi-scale simulation of crack propagation in the ductile-brittle transition region." Doctoral thesis, Technische Universitaet Bergakademie Freiberg Universitaetsbibliothek "Georgius Agricola", 2013. http://nbn-resolving.de/urn:nbn:de:bsz:105-qucosa-121281.

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In the present thesis the crack propagation in the ductile-brittle transition region is studied on two scales with deterministic models. In the macroscopic model the ductile failure is described by a non-local Gurson-model whereas the discrete void microstructure is resolved around the crack tip in the microscopic model. The failure by cleavage is not evaluated by means of a post-processing criterion but is modeled equivalently using a cohesive zone model on both scales. Thus, cleavage is not a priori identified with unstable crack propagation but the transition between stable and unstable mod
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Books on the topic "Multi-scale classifications"

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Po, Ming Jack. Multi-scale Representations for Classification of Protein Crystal Images and Multi-Modal Registration of the Lung. [publisher not identified], 2015.

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Book chapters on the topic "Multi-scale classifications"

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Bailey, Robert G. "Multi-Scale Ecosystem Analysis." In Global to Local: Ecological Land Classification. Springer Netherlands, 1996. http://dx.doi.org/10.1007/978-94-009-1653-1_3.

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Ciortan, Irina Mihaela, Yoko Arteaga, Sony George, and Jon Yngve Hardeberg. "Multi-scale Painter Classification." In Communications in Computer and Information Science. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-20302-2_12.

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Xu, Haotian, Xiaobo Jin, Qiufeng Wang, and Kaizhu Huang. "Multi-scale Attention Consistency for Multi-label Image Classification." In Communications in Computer and Information Science. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-63820-7_93.

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Li, Shaohua, Yong Liu, Xiuchao Sui, et al. "Multi-Instance Multi-Scale CNN for Medical Image Classification." In Lecture Notes in Computer Science. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-32251-9_58.

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Li, Wenbin. "Learning Multi-scale Representations for Material Classification." In Lecture Notes in Computer Science. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-11752-2_65.

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Hébrail, Georges, and Yves Lechevallier. "Building Small Scale Models of Multi-Entity Databases By Clustering." In Classification, Clustering, and Data Mining Applications. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-642-17103-1_37.

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Qiu, Zhen, Yifan Zhang, Fei Li, Xiulan Zhang, Yanwu Xu, and Mingkui Tan. "Multi-scale Multi-target Domain Adaptation for Angle Closure Classification." In Pattern Recognition and Computer Vision. Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-18910-4_7.

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Wang, Yan, Lingxi Xie, Siyuan Qiao, Ya Zhang, Wenjun Zhang, and Alan L. Yuille. "Multi-scale Spatially-Asymmetric Recalibration for Image Classification." In Computer Vision – ECCV 2018. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-01261-8_31.

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Chaudhary, Priyanshu, Nischay Dhankhar, Amit Singhal, and K. P. S. Rana. "Multi-scale Architecture for High Performance RSVP Classification." In Lecture Notes in Electrical Engineering. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-2508-3_32.

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Wang, Lin, Ammar Hawbani, and Yan Xiong. "A Multi-scale Multi-modal Multi-dimension Joint Transformer for Two-Stream Action Classification." In PRICAI 2023: Trends in Artificial Intelligence. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-7025-4_19.

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Conference papers on the topic "Multi-scale classifications"

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Zhou, Zhenghao, Yisu Wang, Fengming Zhao, and Ziyu Jia. "Wavelet Multi-level Multi-scale Neural Network for EEG Classification." In 2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2024. https://doi.org/10.1109/bibm62325.2024.10822364.

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Jiang, Wei, Xu Tang, Zhixi Feng, et al. "Multi-Scale Sparse Transformer for Remote Sensing Scene Classification." In IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2024. http://dx.doi.org/10.1109/igarss53475.2024.10642862.

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Tan, Ping, Jia Li, Guoyun Zhang, and Lin Zhao. "Multi-Scale and Multi-Modal Contrastive Learning for Hyperspectral and LiDAR Classification." In 2024 2nd International Conference on Computer, Vision and Intelligent Technology (ICCVIT). IEEE, 2024. https://doi.org/10.1109/iccvit63928.2024.10872424.

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Ma, Wenrui, Qinjun Zhao, Jinsheng Ji, Kang Zhao, and Lijun Yang. "Multi-head Attention and Multi-scale Feature Fusion for Remote Sensing Image Classification." In 2025 IEEE 8th Information Technology and Mechatronics Engineering Conference (ITOEC). IEEE, 2025. https://doi.org/10.1109/itoec63606.2025.10968974.

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Pasek Suta Wijaya, I. Gede, Keiichi Uchimura, and Gou Koutaki. "Multi pose face recognition using double stages classifications: SMLDA and fusion of scale invariant features." In 2013 19th Korea-Japan Joint Workshop on Frontiers of Computer Vision (FCV2013). IEEE, 2013. http://dx.doi.org/10.1109/fcv.2013.6485477.

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REED, TB, D. SMITH, and BC TUCHOLKE. "ABSTRACT - MULTI-SOURCE, MULTI-SCALE IMAGERY OF THE MAR, 25-27° NORTH." In Acoustic Classification and Mapping of the Seabed 1993. Institute of Acoustics, 2024. http://dx.doi.org/10.25144/20457.

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Cheng, Gong, Decheng Gao, Yang Liu, and Junwei Han. "Multi-scale and Discriminative Part Detectors Based Features for Multi-label Image Classification." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/90.

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Convolutional neural networks (CNNs) have shown their promise for image classification task. However, global CNN features still lack geometric invariance for addressing the problem of intra-class variations and so are not optimal for multi-label image classification. This paper proposes a new and effective framework built upon CNNs to learn Multi-scale and Discriminative Part Detectors (MsDPD)-based feature representations for multi-label image classification. Specifically, at each scale level, we (i) first present an entropy-rank based scheme to generate and select a set of discriminative par
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Rahtu, E., and J. Heikkila. "Object classification with multi-scale autoconvolution." In Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004. IEEE, 2004. http://dx.doi.org/10.1109/icpr.2004.1334463.

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Wang, Yunlong, Lu Yang, Yukun Li, and Lei Fu. "Multi-Scale Dynamic Convolution for Classification." In 2021 IEEE 4th International Conference on Information Systems and Computer Aided Education (ICISCAE). IEEE, 2021. http://dx.doi.org/10.1109/iciscae52414.2021.9590720.

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Wang, Shiyao, Minlie Huang, and Zhidong Deng. "Densely Connected CNN with Multi-scale Feature Attention for Text Classification." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/621.

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Text classification is a fundamental problem in natural language processing. As a popular deep learning model, convolutional neural network (CNN) has demonstrated great success in this task. However, most existing CNN models apply convolution filters of fixed window size, thereby unable to learn variable n-gram features flexibly. In this paper, we present a densely connected CNN with multi-scale feature attention for text classification. The dense connections build short-cut paths between upstream and downstream convolutional blocks, which enable the model to compose features of larger scale f
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Reports on the topic "Multi-scale classifications"

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Engel, Bernard, Yael Edan, James Simon, Hanoch Pasternak, and Shimon Edelman. Neural Networks for Quality Sorting of Agricultural Produce. United States Department of Agriculture, 1996. http://dx.doi.org/10.32747/1996.7613033.bard.

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The objectives of this project were to develop procedures and models, based on neural networks, for quality sorting of agricultural produce. Two research teams, one in Purdue University and the other in Israel, coordinated their research efforts on different aspects of each objective utilizing both melons and tomatoes as case studies. At Purdue: An expert system was developed to measure variances in human grading. Data were acquired from eight sensors: vision, two firmness sensors (destructive and nondestructive), chlorophyll from fluorescence, color sensor, electronic sniffer for odor detecti
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Asari, Vijayan, Paheding Sidike, Binu Nair, Saibabu Arigela, Varun Santhaseelan, and Chen Cui. PR-433-133700-R01 Pipeline Right-of-Way Automated Threat Detection by Advanced Image Analysis. Pipeline Research Council International, Inc. (PRCI), 2015. http://dx.doi.org/10.55274/r0010891.

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A novel algorithmic framework for the robust detection and classification of machinery threats and other potentially harmful objects intruding onto a pipeline right-of-way (ROW) is designed from three perspectives: visibility improvement, context-based segmentation, and object recognition/classification. In the first part of the framework, an adaptive image enhancement algorithm is utilized to improve the visibility of aerial imagery to aid in threat detection. In this technique, a nonlinear transfer function is developed to enhance the processing of aerial imagery with extremely non-uniform l
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