A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Context-based vision system for place and object recognition
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
The challenge problem for automated detection of 101 semantic concepts in multimedia
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
Representing shape with a spatial pyramid kernel
Proceedings of the 6th ACM international conference on Image and video retrieval
(Un)Reliability of video concept detection
CIVR '08 Proceedings of the 2008 international conference on Content-based image and video retrieval
Object Categorization Using Hierarchical Wavelet Packet Texture Descriptors
ISM '09 Proceedings of the 2009 11th IEEE International Symposium on Multimedia
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In this paper, based on probability distribution of weak classifier output, an improved Adaboost-based multi-classifiers fusion algorithm is proposed for semantic concept extraction. We present a novel method to compute the error rate and the weight of each classifier. We believe that the error rate of an example should be related to its rank in a weak classifier output. First, the probability distribution of the SVM output is estimated. SVM is regarded as the weak classifier in our system. Then, based on the negative and positive examples probability distributions, we can calculate the error rates of positive and negative example respectively. We define the error rate of a positive example as the proportion of negative examples whose scores are bigger than this positive example in an SVM output. Finally, we integrate the error rate into the Adaboost algorithm and add some modification to further improve our performance. We call the proposed fusion method D-Adaboost since the distribution-based error rate computing algorithm is integrated. Experimental results on TRECVID-2007 dataset show the effectiveness of the proposed D-Adaboost.