Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Evaluation campaigns and TRECVid
MIR '06 Proceedings of the 8th ACM international workshop on Multimedia information retrieval
Grid'5000: A Large Scale And Highly Reconfigurable Experimental Grid Testbed
International Journal of High Performance Computing Applications
Evaluation of active learning strategies for video indexing
Image Communication
Image and video indexing using networks of operators
Journal on Image and Video Processing
Semisupervised SVM batch mode active learning with applications to image retrieval
ACM Transactions on Information Systems (TOIS)
TakTuk, adaptive deployment of remote executions
Proceedings of the 18th ACM international symposium on High performance distributed computing
IEEE Transactions on Knowledge and Data Engineering
Fast Incremental Learning Algorithm of SVM on KKT Conditions
FSKD '09 Proceedings of the 2009 Sixth International Conference on Fuzzy Systems and Knowledge Discovery - Volume 01
Improving SVM Classification on Imbalanced Data Sets in Distance Spaces
ICDM '09 Proceedings of the 2009 Ninth IEEE International Conference on Data Mining
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With our previous research, active learning with multi-classifier showed considering performance in large scale data but much calculation was involved. In this paper, we proposed an incremental multi-classifier (SVM classifiers were used) learning algorithm for large scale imbalanced image annotation. For further accelerating the training and predicting process, Grid'5000, French National Grid, was adopted. The result show that the best performance was reached with only 15-30% of the corpus annotated and our new method could achieve almost the same precision while save nearly 50-60% or even more than 94% of the calculation time when parallel multi-threads were used. Our proposed method will be much potential on very large scale data for less processing time.