A Noise Robust Batch Mode Semi-supervised and Active Learning Framework for Image Classification

2019 ◽  
pp. 541-552
Author(s):  
Chaoqun Hou ◽  
Chenhui Yang ◽  
Fujia Ren ◽  
Rongjie Lin
Keyword(s):  
Active Learning ◽  
Image Classification ◽  
Batch Mode ◽  
Learning Framework ◽  
Noise Robust

scholarly journals Fuzziness-based active learning framework to enhance hyperspectral image classification performance for discriminative and generative classifiers

PLoS ONE ◽  
2018 ◽  
Vol 13 (1) ◽  
pp. e0188996 ◽  
Author(s):  
Muhammad Ahmad ◽  
Stanislav Protasov ◽  
Adil Mehmood Khan ◽  
Rasheed Hussain ◽  
Asad Masood Khattak ◽  
...  
Keyword(s):  
Active Learning ◽  
Image Classification ◽  
Hyperspectral Image ◽  
Classification Performance ◽  
Hyperspectral Image Classification ◽  
Learning Framework

Batch Mode Active Learning for Geographical Image Classification

2015 ◽  
pp. 744-755
Author(s):  
Zengmao Wang ◽  
Bo Du ◽  
Lefei Zhang ◽  
Wenbin Hu ◽  
Dacheng Tao ◽  
...  
Keyword(s):  
Active Learning ◽  
Image Classification ◽  
Batch Mode

A Semi-Supervised Active Learning Framework for Image Classification

2014 ◽  
Vol 556-562 ◽  
pp. 4765-4769
Author(s):  
Han Yi Li ◽  
Ming Yang ◽  
Nan Nan Kang ◽  
Lu Lu Yue
Keyword(s):  
Active Learning ◽  
Image Classification ◽  
Supervised Learning ◽  
The Other ◽  
Training Data ◽  
Classification Error ◽  
Classification Algorithms ◽  
Learning Framework ◽  
User Query ◽  
Changing Rate

In this paper, a novel image classification method, incorporating active learning and semi-supervised learning (SSL), is proposed. In this method, two classifiers are needed where one is trained by labeled data and some unlabeled data, while the other one is trained only by labeled data. Specifically, in each round, two classifiers iterate to select useful examples in contention for user query. Then we compute the label changing rate for every unlabeled example in each classifier. Those examples in which the label changing rate is zero and the label in the two classifiers is the same are selected to add into the training data of the first classifier. Our experimental results show that our method significantly reduced the need of labeled examples, while at the same time reducing classification error compared with widely used image classification algorithms.

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