Relay boost fusion for learning rare concepts in multimedia

Authors:
Dong Wang;Jianmin Li;Bo Zhang
Affiliations:
State Key Laboratory of Intelligent Technology and System, Department of Computer Science and Technology, Tsinghua University, Beijing, P.R. China;State Key Laboratory of Intelligent Technology and System, Department of Computer Science and Technology, Tsinghua University, Beijing, P.R. China;State Key Laboratory of Intelligent Technology and System, Department of Computer Science and Technology, Tsinghua University, Beijing, P.R. China
Venue:
CIVR'06 Proceedings of the 5th international conference on Image and Video Retrieval
Year:
2006

Citing 11
Cited 5

AdaCost: Misclassification Cost-Sensitive Boosting

ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Evaluating Boosting Algorithms to Classify Rare Classes: Comparison and Improvements

ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
An efficient boosting algorithm for combining preferences

The Journal of Machine Learning Research
Mining with rarity: a unifying framework

ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
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
Learning the semantics of multimedia queries and concepts from a small number of examples

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
SMOTE: synthetic minority over-sampling technique

Journal of Artificial Intelligence Research
Learning when training data are costly: the effect of class distribution on tree induction

Journal of Artificial Intelligence Research
LIBSVM: A library for support vector machines

ACM Transactions on Intelligent Systems and Technology (TIST)

The feature and spatial covariant kernel: adding implicit spatial constraints to histogram

Proceedings of the 6th ACM international conference on Image and video retrieval
Video search in concept subspace: a text-like paradigm

Proceedings of the 6th ACM international conference on Image and video retrieval
Video diver: generic video indexing with diverse features

Proceedings of the international workshop on Workshop on multimedia information retrieval
Learning structured concept-segments for interactive video retrieval

CIVR '08 Proceedings of the 2008 international conference on Content-based image and video retrieval
The Pascal Visual Object Classes (VOC) Challenge

International Journal of Computer Vision

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper relates learning rare concepts for multimedia retrieval to a more general setting of imbalanced data. A Relay Boost (RL.Boost) algorithm is proposed to solve this imbalanced data problem by fusing multiple features extracted from the multimedia data. As a modified RankBoost algorithm, RL.Boost directly minimizes the ranking loss, rather than the classification error. RL.Boost also iteratively samples positive/negative pairs for a more balanced data set to get diverse weak ranking with different features, and combines them in a ranking ensemble. Experiments on the standard TRECVID 2005 benchmark data set show the effectiveness of the proposed algorithm.