Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
Exact simplification of support vector solutions
The Journal of Machine Learning Research
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Training linear SVMs in linear time
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Large-Scale Concept Ontology for Multimedia
IEEE MultiMedia
Fast Kernel Classifiers with Online and Active Learning
The Journal of Machine Learning Research
Pegasos: Primal Estimated sub-GrAdient SOlver for SVM
Proceedings of the 24th international conference on Machine learning
A dual coordinate descent method for large-scale linear SVM
Proceedings of the 25th international conference on Machine learning
A simple and efficient sampling method for estimating AP and NDCG
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
LIBLINEAR: A Library for Large Linear Classification
The Journal of Machine Learning Research
Kernel Codebooks for Scene Categorization
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part III
Today's and tomorrow's retrieval practice in the audiovisual archive
Proceedings of the ACM International Conference on Image and Video Retrieval
Evaluating Color Descriptors for Object and Scene Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
A fast dual method for HIK SVM learning
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part II
Image classification using super-vector coding of local image descriptors
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part V
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
SURF: speeded up robust features
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
Efficient Additive Kernels via Explicit Feature Maps
IEEE Transactions on Pattern Analysis and Machine Intelligence
Empowering Visual Categorization With the GPU
IEEE Transactions on Multimedia
Power mean SVM for large scale visual classification
CVPR '12 Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
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Many emerging application areas in video and image processing require large-scale visual concept detection. Examples include content-based indexing of online user-generated videos and 24/7 archival of TV broadcasts. The current state of the art in concept detection uses bag-of-visual-words features with computationally heavy exponential kernel classifiers. We argue that this classifier approach is not feasible for large-scale real-time applications, and propose instead to use combinations of approximate additive kernel classifiers. By using explicit kernel maps and the power mean SVM, followed by fusion of classifiers trained on different features, we achieve high retrieval precision while retaining real-time performance for large sets of concepts. This paper presents a series of experiments with the large-scale TRECVID 2012 video database and the commonly used Fifteen Scene Categories image database. We show significantly improved retrieval performance over standard linear classifiers, and by late fusion over several visual features, the approximative additive kernels outperform any single exponential kernel in only a fraction of the detection time.