Incremental Multiple Classifier Active Learning for Concept Indexing in Images and Videos

Active learning with multiple classifiers has shown good performance for concept indexing in images or video shots in the case of highly imbalanced data. It involves however a large number of computations. In this paper, we propose a new incremental active learning algorithm based on multiple SVM for image and video annotation. The experimental result show that the best performance (MAP) is reached when 15-30% of the corpus is annotated and the new method can achieve almost the same precision while saving 50 to 63% of the computation time.

[1]  C. Lee Giles,et al.  Active learning for class imbalance problem , 2007, SIGIR.

[2]  Georges Quénot,et al.  Evaluations of multi-learner approaches for concept indexing in video documents , 2010, RIAO.

[3]  Ying Zhang,et al.  A Fast Support Vector Machine Classification Algorithm Based on Karush-Kuhn-Tucker Conditions , 2009, RSFDGrC.

[4]  Hervé Glotin,et al.  IRIM at TRECVID2009: High Level Feature Extraction , 2009 .

[5]  Fabio Roli,et al.  Multiple Classifier Systems, 9th International Workshop, MCS 2010, Cairo, Egypt, April 7-9, 2010. Proceedings , 2010, MCS.

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

[7]  Guoyin Wang,et al.  Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing , 2013, Lecture Notes in Computer Science.

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

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

[10]  Marcel Worring,et al.  Learning rich semantics from news video archives by style analysis , 2006, TOMCCAP.

[11]  John R. Smith,et al.  On the detection of semantic concepts at TRECVID , 2004, MULTIMEDIA '04.

[12]  Josef Kittler,et al.  Concept learning for image and video retrieval: The inverse random under sampling approach , 2009, 2009 17th European Signal Processing Conference.

[13]  Georges Quénot,et al.  Active learning with multiple classifiers for multimedia indexing , 2010, 2010 International Workshop on Content Based Multimedia Indexing (CBMI).

[14]  Dana Angluin,et al.  Queries and concept learning , 1988, Machine Learning.

[15]  C. Lee Giles,et al.  Learning on the border: active learning in imbalanced data classification , 2007, CIKM '07.

[16]  Jason Weston,et al.  Fast Kernel Classifiers with Online and Active Learning , 2005, J. Mach. Learn. Res..

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

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

[19]  Christopher M. Bishop,et al.  Pattern Recognition and Machine Learning (Information Science and Statistics) , 2006 .

[20]  Yimin Wen,et al.  Incremental Learning of Support Vector Machines by Classifier Combining , 2007, PAKDD.

[21]  Josef Kittler,et al.  A Multiple Expert Approach to the Class Imbalance Problem Using Inverse Random under Sampling , 2009, MCS.

[22]  Nasser M. Nasrabadi,et al.  Pattern Recognition and Machine Learning , 2006, Technometrics.

[23]  Stefan Rüping,et al.  Incremental Learning with Support Vector Machines , 2001, ICDM.

[24]  Marimuthu Palaniswami,et al.  Incremental training of support vector machines , 2005, IEEE Transactions on Neural Networks.