Filters








1,605 Hits in 3.0 sec

Table of contents

2020 IEEE Transactions on Neural Networks and Learning Systems  
Tang Discriminative Fast Hierarchical Learning for Multiclass Image Classification .............................................. .......................................................................  ...  Tao Radial-Based Oversampling for Multiclass Imbalanced Data Classification .................................................. ..........................................................................  ... 
doi:10.1109/tnnls.2020.3009705 fatcat:4cm6xswfnrarzezba53tulu62q

Towards Optimal Discriminating Order for Multiclass Classification

Dong Liu, Shuicheng Yan, Yadong Mu, Xian-Sheng Hua, Shih-Fu Chang, Hong-Jiang Zhang
2011 2011 IEEE 11th International Conference on Data Mining  
In this paper, we investigate how to design an optimized discriminating order for boosting multiclass classification.  ...  The main idea is to optimize a binary tree architecture, referred to as Sequential Discriminating Tree (SDT), that performs the multiclass classification through a hierarchical sequence of coarse-to-fine  ...  Image Categorization Image categorization is one of the natural applications of multiclass classification.  ... 
doi:10.1109/icdm.2011.147 dblp:conf/icdm/LiuYMHCZ11 fatcat:tgts5yk5ynbgxjg6bmpob7775y

Recycled linear classifiers for multiclass classification

Akshay Soni, Jarvis Haupt, Fatih Porikli
2014 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)  
Many machine learning applications employ a multiclass classification stage that uses multiple binary linear classifiers as building blocks.  ...  Among these, commonly used strategies such as onevs-one classification can require learning a large number of hyperplanes, even when the number of classes to be discriminated among is modest.  ...  Hierarchical Classifiers (HC) are also commonly used for multiclass classification tasks.  ... 
doi:10.1109/icassp.2014.6854142 dblp:conf/icassp/SoniHP14 fatcat:vpgofgfpzbbthpphwaefboiu6i

Orientation Invariant Features for Multiclass Object Recognition [chapter]

Michael Villamizar, Alberto Sanfeliu, Juan Andrade-Cetto
2006 Lecture Notes in Computer Science  
We present a framework for object recognition based on simple scale and orientation invariant local features that when combined with a hierarchical multiclass boosting mechanism produce robust classifiers  ...  To allow for efficient rotation invariance, we propose the use of non-Gaussian steerable filters, together with an Orientation Integral Image for a speedy computation of local orientation.  ...  In this work we investigate on the use of similar multiclass feature selection, but with keen interest in fast computation of orientation invariant weak classifiers [6] for multiclass rotation invariant  ... 
doi:10.1007/11892755_68 fatcat:s5ub7cnfyfcujdba3l5gfc326u

Multiclass feature learning for hyperspectral image classification: Sparse and hierarchical solutions

Devis Tuia, Rémi Flamary, Nicolas Courty
2015 ISPRS journal of photogrammetry and remote sensing (Print)  
To this end, we consider a fast and linear classifier, multiclass logistic classification, and show that with a good representation (the filters discovered), such a simple classifier can reach at least  ...  We apply the proposed active set learner in four hyperspectral image classification problems, including agricultural and urban classification at different resolutions, as well as multimodal data.  ...  Acknowledgements 646 This work has been supported by the Swiss National Sci- Prasad, for providing the Houston data.  ... 
doi:10.1016/j.isprsjprs.2015.01.006 fatcat:fabip6auhzblfhfezzhfr3flby

Bayesian Efficient Multiple Kernel Learning [article]

Mehmet Gonen
2012 arXiv   pre-print
We briefly explain how the proposed method can be extended for multiclass learning and semi-supervised learning.  ...  Experiments with large numbers of kernels on benchmark data sets show that our inference method is quite fast, requiring less than a minute.  ...  We shortly explain two variants for multiclass learning and semi-supervised learning. Multiclass Learning In multiclass learning, we consider classification problems with more than two classes.  ... 
arXiv:1206.6465v1 fatcat:2jqr2524czh4vaiotl62rdm7pi

People recognition and pose estimation in image sequences

C. Nakajima, M. Pontil, T. Poggio
2000 Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium  
This paper presents a system which learns from examples to automatically recognize people and estimate their poses in image sequences with the potential application to daily surveillance in indoor environments  ...  The person in the image is represented by a set of features based on color and shape information.  ...  The core of the system is a multiclass classification problem which we have approached using two types of hierarchical SVM classifiers.  ... 
doi:10.1109/ijcnn.2000.860771 dblp:conf/ijcnn/NakajimaPP00 fatcat:7k67peaffbfwdahxn55gp56svy

Integrating Concept Ontology and Multitask Learning to Achieve More Effective Classifier Training for Multilevel Image Annotation

Jianping Fan, Yuli Gao, Hangzai Luo
2008 IEEE Transactions on Image Processing  
a multiple kernel learning algorithm is developed for SVM image classifier training.  ...  To tackle the problem of huge intraconcept visual diversity for the image concepts at the higher levels of the concept ontology, a novel hierarchical boosting algorithm is developed to learn their ensemble  ...  Schonfeld for handling the review process of this paper.  ... 
doi:10.1109/tip.2008.916999 pmid:18270128 fatcat:bxokhjalpzcfdpwyk3yajzl3ni

Efficient Discriminative Learning of Class Hierarchy for Many Class Prediction [chapter]

Lin Chen, Lixin Duan, Ivor W. Tsang, Dong Xu
2013 Lecture Notes in Computer Science  
Recently the maximum margin criterion has been employed to learn a discriminative class hierarchical model, which shows promising performance for rapid multi-class prediction.  ...  To address this issue, in this paper we propose a new efficient discriminative class hierarchy learning approach for many class prediction.  ...  Related Works In this section, we review some existing methods for multiclass classification.  ... 
doi:10.1007/978-3-642-37331-2_21 fatcat:uzdbecaakvay7bfxpgt6f2qnay

Robust Multi-view Face Detection Using Error Correcting Output Codes [chapter]

Hongming Zhang, Wen Gao, Xilin Chen, Shiguang Shan, Debin Zhao
2006 Lecture Notes in Computer Science  
In addition, the binary classifier is constructed as a coarse to fine procedure using fast histogram matching followed by accurate Support Vector Machine (SVM).  ...  Besides applying ECOC to multi-view face detection, this paper emphasizes on designing efficient binary classifiers by learning informative features through minimizing the error rate of the ensemble ECOC  ...  Acknowledgements This research is supported by National Nature Science Foundation of China (60332010), 100 Talents Program of CAS, China, the Program for New Century Excellent Talents in University (NCET  ... 
doi:10.1007/11744085_1 fatcat:oz7lebyi2jcvvi4pkllajgh5vy

A decision support system for multimodal brain tumor classification using deep learning

Muhammad Imran Sharif, Muhammad Attique Khan, Musaed Alhussein, Khursheed Aurangzeb, Mudassar Raza
2021 Complex & Intelligent Systems  
In this article, a new automated deep learning method is proposed for the classification of multiclass brain tumors.  ...  AbstractMulticlass classification of brain tumors is an important area of research in the field of medical imaging.  ...  In CNN architecture, end-to-end learning is performed for the hierarchical representation of an input image.  ... 
doi:10.1007/s40747-021-00321-0 fatcat:c6dwvh5n2rgo3ptwseyerwhtgu

Multiclass classification of FDG PET scans for the distinction between Parkinson's disease and atypical parkinsonian syndromes

Gaëtan Garraux, Christophe Phillips, Jessica Schrouff, Alexandre Kreisler, Christian Lemaire, Christian Degueldre, Christian Delcour, Roland Hustinx, André Luxen, Alain Destée, Eric Salmon
2013 NeuroImage: Clinical  
Here, we used relevance vector machine (RVM) in combination with booststrap resampling ('bagging') for nonhierarchical multiclass classification.  ...  APS) and 90%, 87%, 79% and 94%, respectively, using multiclass RVM (PD vs. MSA vs. PSP vs. CBS). Multiclass RVM achieved 45%, 55% and 62% classification accuracy for, MSA, PSP and CBS, respectively.  ...  Georges Franck for patient referrals and Mrs Aurélie Dessoullières and Annick Claes for their help in data management.  ... 
doi:10.1016/j.nicl.2013.06.004 pmid:24179839 pmcid:PMC3778264 fatcat:3dd7xgj77zgynegfyueca74zre

Stacked Convolutional Neural Network for Diagnosis of COVID-19 Disease from X-ray Images [article]

Mahesh Gour, Sweta Jain
2020 arXiv   pre-print
The proposed stacked CNN achieves an accuracy of 92.74%, the sensitivity of 93.33%, PPV of 92.13%, and F1-score of 0.93 for the classification of X-ray images.  ...  The proposed CNN model combines the discriminating power of the different CNN's sub-models and classifies chest X-ray images into COVID-19, Normal, and Pneumonia classes.  ...  Their hierarchical classification approach achieved F1-Score of 0.89 for the COVID-19 identification in the X-ray images.  ... 
arXiv:2006.13817v1 fatcat:mwypgj7llfh3veljpq54wcgfbe

Learning Fine-grained Features via a CNN Tree for Large-scale Classification [article]

Zhenhua Wang, Xingxing Wang, Gang Wang
2017 arXiv   pre-print
Such features are expected to be more discriminative, compared to features learned for all the classes.  ...  Experiments on large-scale image classification tasks demonstrate that our method could boost the performance of a given basic CNN model.  ...  In this paper, we present a novel approach to enhance the discriminability of CNN for large-scale multiclass classification.  ... 
arXiv:1511.04534v2 fatcat:drf5nra5mndhtjqclmee3tjbcm

Discriminant ECOC: a heuristic method for application dependent design of error correcting output codes

O. Pujol, P. Radeva, J. Vitria
2006 IEEE Transactions on Pattern Analysis and Machine Intelligence  
We present a heuristic method for learning error correcting output codes matrices based on a hierarchical partition of the class space that maximizes a discriminative criterion.  ...  Our method is validated using the UCI database and applied to a real problem, the classification of traffic sign images.  ...  DISCRIMINANT ECOC Discriminant ECOC is born as an answer to three demands: First, a heuristic for the design of the ECOC matrix, second, the search for high-performance classification using the minimum  ... 
doi:10.1109/tpami.2006.116 pmid:16724594 fatcat:x4nn5bgg4fbghidxcazgfjoeam
« Previous Showing results 1 — 15 out of 1,605 results