Enhancing SVM Active Learning for Image Retrieval Using Semi-supervised Bias-Ensemble

A kernel-based active learning strategy for content-based image retrieval

2010 International Workshop on Content Based Multimedia Indexing (CBMI) ◽

10.1109/cbmi.2010.5529915 ◽

2010 ◽

Author(s):

I. Daoudi ◽

K. Idrissi

Keyword(s):

Active Learning ◽

Image Retrieval ◽

Learning Strategy ◽

Content Based Image Retrieval ◽

Active Learning Strategy

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Bootstrapping SVM active learning by incorporating unlabelled images for image retrieval

2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings. ◽

10.1109/cvpr.2003.1211412 ◽

2003 ◽

Cited By ~ 1

Author(s):

Lei Wang ◽

Kap Luk Chan ◽

Zhihua Zhang

Keyword(s):

Active Learning ◽

Image Retrieval

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Stochastic exploration and active learning for image retrieval

Image and Vision Computing ◽

10.1016/j.imavis.2006.01.004 ◽

2007 ◽

Vol 25 (1) ◽

pp. 14-23 ◽

Cited By ~ 31

Author(s):

Matthieu Cord ◽

Philippe H. Gosselin ◽

Sylvie Philipp-Foliguet

Keyword(s):

Active Learning ◽

Image Retrieval

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A Graph-Based Active Learning Approach Using Forest Classifier for Image Retrieval

Proceedings of the International Conference on Data Engineering and Communication Technology - Advances in Intelligent Systems and Computing ◽

10.1007/978-981-10-1678-3_11 ◽

2016 ◽

pp. 119-129

Author(s):

Shrikant Dhawale ◽

Bela Joglekar ◽

Parag Kulkarni

Keyword(s):

Active Learning ◽

Image Retrieval ◽

Learning Approach

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Active learning for social image retrieval using Locally Regressive Optimal Design

Neurocomputing ◽

10.1016/j.neucom.2011.06.037 ◽

2012 ◽

Vol 95 ◽

pp. 54-59 ◽

Cited By ~ 5

Author(s):

Yinfu Feng ◽

Jun Xiao ◽

Zhengjun Zha ◽

Hong Zhang ◽

Yi Yang

Keyword(s):

Optimal Design ◽

Active Learning ◽

Image Retrieval ◽

Social Image ◽

Social Image Retrieval

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Online Content-Based Image Retrieval Using Active Learning

Machine Learning Techniques for Multimedia - Cognitive Technologies ◽

10.1007/978-3-540-75171-7_5 ◽

2008 ◽

pp. 115-138 ◽

Cited By ~ 1

Author(s):

Matthieu Cord ◽

Philippe-Henri Gosselin

Keyword(s):

Active Learning ◽

Image Retrieval ◽

Content Based Image Retrieval ◽

Online Content

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Medical Image Retrieval Based on Semi-Supervised Learning

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.108-111.201 ◽

2010 ◽

Vol 108-111 ◽

pp. 201-206 ◽

Cited By ~ 1

Author(s):

Hui Liu ◽

Cai Ming Zhang ◽

Hua Han

Keyword(s):

Support Vector Machine ◽

Active Learning ◽

Image Retrieval ◽

Supervised Learning ◽

Relevance Feedback ◽

Medical Image ◽

Content Based Image Retrieval ◽

Support Vector ◽

Learning Support ◽

Medical Image Retrieval

Among various content-based image retrieval (CBIR) methods based on active learning, support vector machine(SVM) active learning is popular for its application to relevance feedback in CBIR. However, the regular SVM active learning has two main drawbacks when used for relevance feedback. Furthermore, it’s difficult to collect vast amounts of labeled data and easy for unlabeled data to image examples. Therefore, it is necessary to define conditions to utilize the unlabeled examples enough. This paper presented a method of medical images retrieval about semi-supervised learning based on SVM for relevance feedback in CBIR. This paper also introduced an algorithm about defining two learners, both learners are re-trained after every relevance feedback round, and then each of them gives every image in a rank. Experiments show that using semi-supervised learning idea in CBIR is beneficial, and the proposed method achieves better performance than some existing methods.

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