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Unsupervised learning for image classification based on distribution of hierarchical feature tree

Thach-Thao Duong, Joo-Hwee Lim, Hai-Quan Vu, Jean-Pierre Chevallet
2008 2008 IEEE International Conference on Research, Innovation and Vision for the Future in Computing and Communication Technologies  
In this paper, we present a novel unsupervised model for the image classification based on feature's distribution of particular patches of images.  ...  We observe the distribution of features on the tree to find out which patches are important in term of a particular class.  ...  In general, image classification can be classified into two major approaches: supervised and unsupervised. In this paper, we introduce an unsupervised learning method for this problem.  ... 
doi:10.1109/rivf.2008.4586371 dblp:conf/rivf/DuongLVC08 fatcat:a4esykh5bzhc3lgxtremahx74q

Unsupervised learning models of invariant features in images: Recent developments in multistage architecture approach for object detection

Sonia Mittal
2016 Zenodo  
Object detection and recognition are important problems in computer vision and pattern recognition domain.  ...  In particular, despite significant research efforts focused on meta-heuristic object detection and recognition, robust and reliable object recognition systems in real time remain elusive.  ...  Deep learning Network were found very successful in computer vision problems.  ... 
doi:10.5281/zenodo.3661859 fatcat:q6naogm5nzd7vf3fautqtiwbpa

Unsupervised Visual Representation Learning with Increasing Object Shape Bias [article]

Zhibo Wang, Shen Yan, Xiaoyu Zhang, Niels Lobo
2019 arXiv   pre-print
In this article, we purpose a novel unsupervised learning method based on contrastive predictive coding.  ...  Beside that, since the number of training images could be unlimited amplification, an universal large-scale pre-trained computer vision model is possible in the future.  ...  However, in con- Our aim is to design an unsupervised learning method trast, pre-trained Bert model reached state-of-the-art in ev- that we are able to train any large computer vision  ... 
arXiv:1911.07272v2 fatcat:7tqlzzy5hvabbljccpgwyn7pau

Improving deep convolutional neural networks with unsupervised feature learning

Kien Nguyen, Clinton Fookes, Sridha Sridharan
2015 2015 IEEE International Conference on Image Processing (ICIP)  
In parallel, unsupervised deep learning such as Convolutional Deep Belief Network (CDBN) has also achieved state-of-the-art in many computer vision tasks.  ...  The depth of these supervised networks has enabled learning deeper and hierarchical representation of features.  ...  A multitude of handdesigned features have been explored (such as SIFT [1] and HOG [2] ) and achieved great success in computer vision tasks.  ... 
doi:10.1109/icip.2015.7351206 dblp:conf/icip/NguyenFS15 fatcat:xw2hndbbmjflxbk55visampmwm

Contrastive Learning Based on Transformer for Hyperspectral Image Classification

Xiang Hu, Teng Li, Tong Zhou, Yu Liu, Yuanxi Peng
2021 Applied Sciences  
The experimental results prove that our model can efficiently extract hyperspectral image features in unsupervised situations.  ...  In this paper, we propose a novel unsupervised framework based on a contrastive learning method and a transformer model for hyperspectral image classification.  ...  Data Availability Statement: The datasets involved in this paper are all public datasets.  ... 
doi:10.3390/app11188670 fatcat:vq4s7lw6hbgffjipaw2cwypkmq

Cross-View Action Recognition via a Transferable Dictionary Pair

Jingjing Zheng, Zhuolin Jiang, Jonathon Phillips, Rama Chellappa
2012 Procedings of the British Machine Vision Conference 2012  
dictionary pair learning Experiment Results Computer Vision Laboratory Experiment Results Computer Vision Laboratory Experiment Results Computer Vision Laboratory Figure 1 .  ...  The accuracy numbers in the bracket are the average recognition accuracies of k-NN without transfer (in black), our unsupervised and supervised approaches (in red).  ... 
doi:10.5244/c.26.125 dblp:conf/bmvc/ZhengJPC12 fatcat:2ihc3mmcj5gxbpshhaaxl6wroa

Diabetic Retinopathy Diagnosis Using ResNet with Fuzzy Rough C-Means Clustering

R. S. Rajkumar, A. Grace Selvarani
2022 Computer systems science and engineering  
If not treated on time it may lead to permanent vision loss in diabetic patients. Today's development in science has no medication to cure Diabetic Retinopathy.  ...  In this paper, we propose an unsupervised clustering technique to automatically cluster the DR into one of its five development stages.  ...  Acknowledgement: We would like to acknowledge the faculties of the department of computer science and Research scholars at Bennett University, Greater Noida, UP who guided during the sabbatical at Bennett  ... 
doi:10.32604/csse.2022.021909 fatcat:4qkegrl5brfvnekwklxrnoyv2y

Convolutional networks and applications in vision

Yann LeCun, Koray Kavukcuoglu, Clement Farabet
2010 Proceedings of 2010 IEEE International Symposium on Circuits and Systems  
Each stage in a ConvNets is composed of a filter bank, some non-linearities, and feature pooling layers. With multiple stages, a ConvNet can learn multi-level hierarchies of features.  ...  A major question for Machine Learning is how to learn such good features automatically.  ...  Despite the recent progress in deep learning, one of the major challenges of computer vision, machine learning, and AI in general in the next decade will be to devise methods that can automatically learn  ... 
doi:10.1109/iscas.2010.5537907 dblp:conf/iscas/LeCunKF10 fatcat:dnus6yikzzbzxjpnbcblkq45o4

Deep Learning Techniques for Complex Problems

Renuka Rajendra B, Sharana Basavana Gowda
2020 Zenodo  
Mimicking the brain is the most challenging task in the field of computer science since its origin.  ...  In this paper we discussed the meaning of deep learning, it's scope, classification and Application. In addition to this we also discussed the future research using deep learning technique.  ...  Computer Vision and Pattern Recognition In following fields the deep learning is used for the maximum extent.  ... 
doi:10.5281/zenodo.3946325 fatcat:iyg4wubv4rdfbad6gggpg6wmze

Self-Supervised Learning for Large-Scale Unsupervised Image Clustering [article]

Evgenii Zheltonozhskii, Chaim Baskin, Alex M. Bronstein, Avi Mendelson
2020 arXiv   pre-print
Self-supervised deep learning has become a strong instrument for representation learning in computer vision. However, those methods have not been evaluated in a fully unsupervised setting.  ...  In this paper, we propose a simple scheme for unsupervised classification based on self-supervised representations.  ...  Deep clustering for unsupervised learning of visual features. In Proceedings of the European Conference on Computer Vision (ECCV), September b.  ... 
arXiv:2008.10312v2 fatcat:y3hxyy4f4rg3dmnlezku4lvb5a

Learning in Computer Vision and Image Understanding

Hayit Greenspan
1993 Neural Information Processing Systems  
There is an increasing interest in the area of Learning in Computer Vision and Image Understanding, both from researchers in the learning community and from researchers involved with the computer vision  ...  Eric Saund of Xerox introduced the window registration problem in unsupervised learning of visual features.  ... 
dblp:conf/nips/Greenspan93 fatcat:qrnzgx267vbhvg62em24zo3om4

VideoMoCo: Contrastive Video Representation Learning with Temporally Adversarial Examples [article]

Tian Pan, Yibing Song, Tianyu Yang, Wenhao Jiang, Wei Liu
2021 arXiv   pre-print
MoCo is effective for unsupervised image representation learning. In this paper, we propose VideoMoCo for unsupervised video representation learning.  ...  Second, we use temporal decay to model key attenuation in the memory queue when computing the contrastive loss.  ...  In IEEE/CVF Inter- Unsupervised feature learning via non-parametric instance national Conference on Computer Vision, 2019. discrimination.  ... 
arXiv:2103.05905v2 fatcat:sf5xbs6n7zgubgdku2yi3j7oae

Radial Basis Function Networks for Convolutional Neural Networks to Learn Similarity Distance Metric and Improve Interpretability

Mohammadreza Amirian, Friedhelm Schwenker
2020 IEEE Access  
However, RBFs have not been integrated into contemporary deep learning research and computer vision using conventional convolutional neural networks (CNNs) due to their lack of adaptability with modern  ...  Finally, we successfully apply RBFs to a range of CNN architectures and evaluate the results on benchmark computer vision datasets.  ...  This paper was made possible by the open source Tensorflow [46] and Keras [47] deep learning libraries, the  ... 
doi:10.1109/access.2020.3007337 fatcat:hpkedsvx6fasxh34cpc56qqibu

Generic Feature Learning in Computer Vision

D. Kanishka Nithin, P. Bagavathi Sivakumar
2015 Procedia Computer Science  
Understanding this, vision community is moving towards learning the optimum features itself instead of learning from the features.  ...  This paper aims to give short overview of deep learning approaches available for vision tasks . We also discuss their applicability (With respect to their properties) in vision field.  ...  Introduction In the recent times most research work in vision field is towards learning low and mid-level features using supervised 2 , unsupervised 3 or fusion of both methods.  ... 
doi:10.1016/j.procs.2015.08.054 fatcat:wsinii7ionfvvomuvr3dhmalp4

A new localized superpixel Markov random field for image segmentation

XiaoFeng Wang, Xiao-Ping Zhang
2009 2009 IEEE International Conference on Multimedia and Expo  
We propose a new localized superpixel Markov random field (SMRF) model to incorporate local data interaction in unsupervised parameter learning.  ...  The advantages of the new model include computational efficiency by using superpixel structure and its ability to integrate local knowledge in the learning process.  ...  Although superpixel concept is widely used in computer vision, it has not been applied to unsupervised learning.  ... 
doi:10.1109/icme.2009.5202578 dblp:conf/icmcs/WangZ09 fatcat:5hqjkwtuobes3d3ojgacwbbfse
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