Content-Based Image Retrieval at the End of the Early Years
IEEE Transactions on Pattern Analysis and Machine Intelligence
Reducing multiclass to binary: a unifying approach for margin classifiers
The Journal of Machine Learning Research
On the detection of semantic concepts at TRECVID
Proceedings of the 12th annual ACM international conference on Multimedia
Multimodal Video Indexing: A Review of the State-of-the-art
Multimedia Tools and Applications
Multimedia semantic indexing using model vectors
ICME '03 Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 1
Evaluation campaigns and TRECVid
MIR '06 Proceedings of the 8th ACM international workshop on Multimedia information retrieval
The challenge problem for automated detection of 101 semantic concepts in multimedia
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
Recent Advances and Open Issues of Digital Image/Video Search
WIAMIS '07 Proceedings of the Eight International Workshop on Image Analysis for Multimedia Interactive Services
Validity and power of t-test for comparing MAP and GMAP
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
A reranking approach for context-based concept fusion in video indexing and retrieval
Proceedings of the 6th ACM international conference on Image and video retrieval
Towards optimal bag-of-features for object categorization and semantic video retrieval
Proceedings of the 6th ACM international conference on Image and video retrieval
Letters: Convex incremental extreme learning machine
Neurocomputing
Extreme support vector machine classifier
PAKDD'08 Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining
Multi-information fusion for uncertain semantic representations of videos
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Adding Semantics to Detectors for Video Retrieval
IEEE Transactions on Multimedia
Engineering Applications of Artificial Intelligence
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Semantic concept detection is an important step in concept-based semantic video retrieval, which can be regarded as an intermediate descriptor to bridge the semantic gap. Most existing concept detection methods utilize Support Vector Machines (SVM) as concept classifier. However, there are several drawbacks of using SVM, such as the high computational cost and large number of parameters to be optimized. In this paper we propose an Extreme Learning Machine (ELM) based Multi-modality Classifier Combination Framework (MCCF) to improve the accuracy of semantic concept detection. In this framework: (i) three ELM classifiers are trained by exploring three kinds of visual features respectively, (ii) a probability-based fusion method is then proposed to combine the prediction results of each ELM classifier, (iii) we integrate the prediction results of ELM classifier with the information of contextual correlation among concepts to further improve the accuracy of semantic concept detection. Experiments on the widely used TRECVID datasets demonstrate that our approach can effectively improve the accuracy of semantic concept detection and achieve performance at extremely high speed.