Incremental multi-classifier learning algorithm on grid'5000 for large scale image annotation

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.

[1]  Rong Jin,et al.  Semisupervised SVM batch mode active learning with applications to image retrieval , 2009, TOIS.

[2]  Haibo He,et al.  Learning from Imbalanced Data , 2009, IEEE Transactions on Knowledge and Data Engineering.

[3]  Stéphane Ayache,et al.  Image and Video Indexing Using Networks of Operators , 2007, EURASIP J. Image Video Process..

[4]  Longin Jan Latecki,et al.  Improving SVM Classification on Imbalanced Data Sets in Distance Spaces , 2009, 2009 Ninth IEEE International Conference on Data Mining.

[5]  Matthijs C. Dorst.  Distinctive Image Features from Scale-Invariant Keypoints , 2011 .

[6]  Olivier Richard,et al.  TakTuk, adaptive deployment of remote executions , 2009, HPDC '09.

[7]  G LoweDavid,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004 .

[8]  Xiaodan Wang,et al.  Fast Incremental Learning Algorithm of SVM on KKT Conditions , 2009, 2009 Sixth International Conference on Fuzzy Systems and Knowledge Discovery.

[9]  Paul Over,et al.  Evaluation campaigns and TRECVid , 2006, MIR '06.

[10]  Taeho Jo,et al.  A Multiple Resampling Method for Learning from Imbalanced Data Sets , 2004, Comput. Intell..

[11]  Stéphane Ayache,et al.  Evaluation of active learning strategies for video indexing , 2007, Signal Process. Image Commun..

[12]  ZhuJianke,et al.  Semisupervised SVM batch mode active learning with applications to image retrieval , 2009 .

[13]  Haibo He,et al.  ADASYN: Adaptive synthetic sampling approach for imbalanced learning , 2008, 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence).

[14]  Dominique Fober,et al.  Work Stealing Scheduler for Automatic Parallelization in Faust-Linux Audio Conference , 2010 .

[15]  Franck Cappello,et al.  Grid'5000: A Large Scale And Highly Reconfigurable Experimental Grid Testbed , 2006, Int. J. High Perform. Comput. Appl..

[16]  M. Shyu,et al.  Florida International University and University of Miami TRECVID 2008 - High Level Feature Extraction , 2008, TRECVID.