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Analysis of activation maps through global pooling measurements for texture classification

https://doi.org/10.1016/j.ins.2020.09.058
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Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/978-3-319-46487-9_5openalex-snapshot
Evaluation of LBP and Deep Texture Descriptors with a New Robustness Benchmark
inferredcites →https://doi.org/10.1007/s10115-015-0821-zopenalex-snapshot
Multiple task transfer learning with small sample sizes
inferredcites →https://doi.org/10.1007/s10994-006-6226-1openalex-snapshot
Extremely randomized trees
inferredcites →https://doi.org/10.1007/s11263-005-4635-4openalex-snapshot
A Statistical Approach to Texture Classification from Single Images
inferredcites →https://doi.org/10.1007/s11263-015-0816-yopenalex-snapshot
ImageNet Large Scale Visual Recognition Challenge
inferredcites →https://doi.org/10.1007/s11263-015-0872-3openalex-snapshot
Deep Filter Banks for Texture Recognition, Description, and Segmentation
inferredcites →https://doi.org/10.1007/s11263-018-1125-zopenalex-snapshot
From BoW to CNN: Two Decades of Texture Representation for Texture Classification
inferredcites →https://doi.org/10.1007/s41095-016-0060-6openalex-snapshot
Texture image classification with discriminative neural networks
inferredcites →https://doi.org/10.1016/j.compbiomed.2015.03.033openalex-snapshot
Computer-aided diagnosis of Myocardial Infarction using ultrasound images with DWT, GLCM and HOS methods: A comparative study
inferredcites →https://doi.org/10.1016/j.patcog.2011.11.009openalex-snapshot
Color texture analysis based on fractal descriptors
inferredcites →https://doi.org/10.1016/j.patcog.2016.08.032openalex-snapshot
Local binary features for texture classification: Taxonomy and experimental study
inferredcites →https://doi.org/10.1016/j.patcog.2017.05.025openalex-snapshot
Handcrafted vs. non-handcrafted features for computer vision classification
inferredcites →https://doi.org/10.1016/j.patrec.2016.08.016openalex-snapshot
Using filter banks in Convolutional Neural Networks for texture classification
inferredcites →https://doi.org/10.1038/nature14539openalex-snapshot
Deep learning
inferredcites →https://doi.org/10.1109/34.531803openalex-snapshot
Texture features for browsing and retrieval of image data
inferredcites →https://doi.org/10.1109/cvpr.2014.222openalex-snapshot
Learning and Transferring Mid-level Image Representations Using Convolutional Neural Networks
inferredcites →https://doi.org/10.1109/cvpr.2016.308openalex-snapshot
Rethinking the Inception Architecture for Computer Vision
inferredcites →https://doi.org/10.1109/cvpr.2016.90openalex-snapshot
Deep Residual Learning for Image Recognition
inferredcites →https://doi.org/10.1109/cvpr.2017.309openalex-snapshot
Deep TEN: Texture Encoding Network
inferredcites →https://doi.org/10.1109/cvpr.2018.00432openalex-snapshot
Large Scale Fine-Grained Categorization and Domain-Specific Transfer Learning
inferredcites →https://doi.org/10.1109/cvpr.2019.00494openalex-snapshot
SpotTune: Transfer Learning Through Adaptive Fine-Tuning
inferredcites →https://doi.org/10.1109/cvpr19808.2013openalex-snapshot
2013 IEEE Conference on Computer Vision and Pattern Recognition
inferredcites →https://doi.org/10.1109/cvpr31182.2015openalex-snapshot
2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
inferredcites →https://doi.org/10.1109/cvpr33180.2016openalex-snapshot
2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
inferredcites →https://doi.org/10.1109/cvpr40276.2018openalex-snapshot
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
inferredcites →https://doi.org/10.1109/cvpr41558.2019openalex-snapshot
2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
inferredcites →https://doi.org/10.1109/cvpr42600.2020.01079openalex-snapshot
EfficientDet: Scalable and Efficient Object Detection
inferredcites →https://doi.org/10.1109/cvprw34339.2014openalex-snapshot
2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops
inferredcites →https://doi.org/10.1109/iccv.2005.54openalex-snapshot
Class-specific material categorisation
inferredcites →https://doi.org/10.1109/icme.2019.00211openalex-snapshot
Easy Transfer Learning By Exploiting Intra-Domain Structures
inferredcites →https://doi.org/10.1109/icme44524.2019openalex-snapshot
2019 IEEE International Conference on Multimedia and Expo (ICME)
inferredcites →https://doi.org/10.1109/icpr.2002.1044854openalex-snapshot
Outex - new framework for empirical evaluation of texture analysis algorithms
inferredcites →https://doi.org/10.1109/sibgrapi-t.2019.00010openalex-snapshot
A Survey of Transfer Learning for Convolutional Neural Networks
inferredcites →https://doi.org/10.1109/sibgrapi-t49140.2019openalex-snapshot
2019 32nd SIBGRAPI Conference on Graphics, Patterns and Images Tutorials (SIBGRAPI-T)
inferredcites →https://doi.org/10.1109/tase.2016.2616485openalex-snapshot
Toward Good Practices for Fine-Grained Maize Cultivar Identification With Filter-Specific Convolutional Activations
inferredcites →https://doi.org/10.1109/tkde.2009.191openalex-snapshot
A Survey on Transfer Learning
inferredcites →https://doi.org/10.1109/tpami.2002.1017623openalex-snapshot
Multiresolution gray-scale and rotation invariant texture classification with local binary patterns
inferredcites →https://doi.org/10.1109/tpami.2005.151openalex-snapshot
A sparse texture representation using local affine regions
inferredcites →https://doi.org/10.1109/tpami.2019.2913372openalex-snapshot
Squeeze-and-Excitation Networks
inferredcites →https://doi.org/10.1145/1273496openalex-snapshot
Proceedings of the 24th international conference on Machine learning
inferredcites →https://doi.org/10.1155/2013/876386openalex-snapshot
New Brodatz-Based Image Databases for Grayscale Color and Multiband Texture Analysis
inferredcites →https://doi.org/10.1167/9.8.784openalex-snapshot
Material perception: What can you see in a brief glance?
inferredcites →https://doi.org/10.1371/journal.pone.0021902openalex-snapshot
Pathways Activated during Human Asthma Exacerbation as Revealed by Gene Expression Patterns in Blood
inferredcites →https://doi.org/10.1371/journal.pone.0027593openalex-snapshot
Cell Motility Dynamics: A Novel Segmentation Algorithm to Quantify Multi-Cellular Bright Field Microscopy Images
inferredcites →https://doi.org/10.1371/journal.pone.0087357openalex-snapshot
Mutual Information between Discrete and Continuous Data Sets
inferredcites →https://doi.org/10.2307/1571915openalex-snapshot
Textures: A Photographic Album for Artists and Designers
inferredcites →https://doi.org/10.23915/distill.00007openalex-snapshot
Feature Visualization
inferredcites →https://doi.org/10.48550/arxiv.1403.6382openalex-snapshot
CNN Features off-the-shelf: an Astounding Baseline for Recognition
inferredcites →https://doi.org/10.48550/arxiv.1409.1556openalex-snapshot
Very Deep Convolutional Networks for Large-Scale Image Recognition
inferredcites →https://doi.org/10.48550/arxiv.2002.05709openalex-snapshot
A Simple Framework for Contrastive Learning of Visual Representations
inferredcites →https://doi.org/10.48550/arxiv.2002.11770openalex-snapshot
Rethinking the Hyperparameters for Fine-tuning

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.2139/ssrn.4180295openalex-extracted
UGAN-2G：An Unsupervised Target Image Remodeling Enhancement Network
inferred← citeshttps://doi.org/10.1007/978-3-030-93052-3_5openalex-snapshot
Pushing the Limits Against the No Free Lunch Theorem: Towards Building General-Purpose (GenP) Classification Systems
inferred← citeshttps://doi.org/10.1007/s11042-022-12048-2openalex-snapshot
Texture image classification based on a pseudo-parabolic diffusion model
inferred← citeshttps://doi.org/10.1007/s13196-026-00425-5openalex-snapshot
Deep features from Pre-trained CNNs and a vision transformer for amazonian timber species recognition using classical machine learning and random image patches
inferred← citeshttps://doi.org/10.1016/j.neucom.2026.133852openalex-snapshot
VORTEX: Challenging CNNs at texture recognition by using vision transformers with orderless and randomized token encodings
inferred← citeshttps://doi.org/10.1016/j.patcog.2023.109802openalex-snapshot
RADAM: Texture recognition through randomized aggregated encoding of deep activation maps
inferred← citeshttps://doi.org/10.1016/j.physa.2023.129086openalex-snapshot
Exploring ordered patterns in the adjacency matrix for improving machine learning on complex networks
inferred← citeshttps://doi.org/10.1038/s40494-025-02066-2openalex-snapshot
Dual-stream multi-layer cross encoding network for texture analysis of architectural heritage elements
inferred← citeshttps://doi.org/10.1038/s41598-025-16456-wopenalex-snapshot
Deep dictionary learning with reconstruction for texture recognition
inferred← citeshttps://doi.org/10.1080/08839514.2021.1878420openalex-snapshot
New Weighted Mean-Based Patterns for Texture Analysis and Classification
inferred← citeshttps://doi.org/10.11606/t.55.2022.tde-25072022-165116openalex-snapshot
Multi-layer analysis of convolutional neural networks for transfer learning applications
inferred← citeshttps://doi.org/10.11606/t.55.2023.tde-03012024-111728openalex-snapshot
Exploring Complex Networks: Matrix-based and Multiscale Approaches for Pattern Recognition
inferred← citeshttps://doi.org/10.3389/fnbot.2024.1489021openalex-snapshot
Cascade contour-enhanced panoptic segmentation for robotic vision perception
inferred← citeshttps://doi.org/10.3390/jimaging7090177openalex-snapshot
Deep Features for Training Support Vector Machines
inferred← citeshttps://doi.org/10.48550/arxiv.2104.03488openalex-snapshot
Deep Features for training Support Vector Machine
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