Machine Learning
Machine Learning
Incremental Learning with Support Vector Machines
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
On the detection of semantic concepts at TRECVID
Proceedings of the 12th annual ACM international conference on Multimedia
Learning rich semantics from news video archives by style analysis
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Evaluation campaigns and TRECVid
MIR '06 Proceedings of the 8th ACM international workshop on Multimedia information retrieval
Fast Kernel Classifiers with Online and Active Learning
The Journal of Machine Learning Research
Active learning for class imbalance problem
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Evaluation of active learning strategies for video indexing
Image Communication
Learning on the border: active learning in imbalanced data classification
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
Image and video indexing using networks of operators
Journal on Image and Video Processing
A Multiple Expert Approach to the Class Imbalance Problem Using Inverse Random under Sampling
MCS '09 Proceedings of the 8th International Workshop on Multiple Classifier Systems
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
A Fast Support Vector Machine Classification Algorithm Based on Karush-Kuhn-Tucker Conditions
RSFDGrC '09 Proceedings of the 12th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing
Incremental learning of support vector machines by classifier combining
PAKDD'07 Proceedings of the 11th Pacific-Asia conference on Advances in knowledge discovery and data mining
Evaluations of multi-learner approaches for concept indexing in video documents
RIAO '10 Adaptivity, Personalization and Fusion of Heterogeneous Information
Incremental training of support vector machines
IEEE Transactions on Neural Networks
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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.