A sequential algorithm for training text classifiers
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
Machine Learning
Machine Learning
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
Early versus late fusion in semantic video analysis
Proceedings of the 13th annual ACM international conference on Multimedia
Early versus late fusion in semantic video analysis
Proceedings of the 13th 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
Evaluation of active learning strategies for video indexing
Image Communication
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
Exploratory undersampling for class-imbalance learning
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Classifier fusion for SVM-based multimedia semantic indexing
ECIR'07 Proceedings of the 29th European conference on IR research
Evaluations of multi-learner approaches for concept indexing in video documents
RIAO '10 Adaptivity, Personalization and Fusion of Heterogeneous Information
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
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We propose and evaluate in this paper a combination of Active Learning and Multiple Classifiers approaches for corpus annotation and concept indexing on highly imbalanced datasets. Experiments were conducted using TRECVID 2008 data and protocol with four different types of video shot descriptors, with two types of classifiers (Logistic Regression and Support Vector Machine with RBF kernel) and with two different active learning strategies (relevance and uncertainty sampling). Results show that the Multiple Classifiers approach significantly increases the effectiveness of the Active Learning. On the considered dataset, the best performance is achieved when 15 to 30% of the corpus is annotated for individual descriptors and when 10 to 15% of the corpus is annotated for their fusion.