Support vector machine active learning for image retrieval
MULTIMEDIA '01 Proceedings of the ninth ACM international conference on Multimedia
Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Support vector machine active learning with applications to text classification
The Journal of Machine Learning Research
Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Large Scale Multiple Kernel Learning
The Journal of Machine Learning Research
More efficiency in multiple kernel learning
Proceedings of the 24th international conference on Machine learning
Bootstrapping SVM active learning by incorporating unlabelled images for image retrieval
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Active learning in multimedia annotation and retrieval: A survey
ACM Transactions on Intelligent Systems and Technology (TIST)
Deep adaptive networks for image classification
ICIMCS '10 Proceedings of the Second International Conference on Internet Multimedia Computing and Service
Multiple kernel active learning for facial expression analysis
ISNN'11 Proceedings of the 8th international conference on Advances in neural networks - Volume Part II
Web media semantic concept retrieval via tag removal and model fusion
ACM Transactions on Intelligent Systems and Technology (TIST) - Survey papers, special sections on the semantic adaptive social web, intelligent systems for health informatics, regular papers
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Recently, multiple kernel learning (MKL) methods have shown promising performance in image classification. As a sort of supervised learning, training MKL-based classifiers relies on selecting and annotating extensive dataset. In general, we have to manually label large amount of samples to achieve desirable MKL-based classifiers. Moreover, MKL also suffers a great computational cost on kernel computation and parameter optimization. In this paper, we propose a local adaptive active learning (LA-AL) method to reduce the labeling and computational cost by selecting the most informative training samples. LA-AL adopts a top-down (or global-local) strategy for locating and searching informative samples. Uncertain samples are first clustered into groups, and then informative samples are consequently selected via inter-group and intra-group competitions. Experiments over COREL-5K show that the proposed LA-AL method can significantly reduce the demand of sample labeling and have achieved the state-of-the-art performance.