Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on 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
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
The challenge problem for automated detection of 101 semantic concepts in multimedia
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
Towards optimal bag-of-features for object categorization and semantic video retrieval
Proceedings of the 6th ACM international conference on Image and video retrieval
How many high-level concepts will fill the semantic gap in news video retrieval?
Proceedings of the 6th ACM international conference on Image and video retrieval
Evaluating bag-of-visual-words representations in scene classification
Proceedings of the international workshop on Workshop on multimedia information retrieval
Scene Classification Using a Hybrid Generative/Discriminative Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
A comparison of color features for visual concept classification
CIVR '08 Proceedings of the 2008 international conference on Content-based image and video retrieval
The SHOGUN Machine Learning Toolbox
The Journal of Machine Learning Research
Sampling strategies for bag-of-features image classification
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
Representations of Keypoint-Based Semantic Concept Detection: A Comprehensive Study
IEEE Transactions on Multimedia
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State-of-the-art systems for visual concept detection typically rely on the Bag-of-Visual-Words representation. While several aspects of this representation have been investigated, such as keypoint sampling strategy, vocabulary size, projection method, weighting scheme or the integration of color, the impact of the spatial extents of local SIFT descriptors has not been studied in previous work. In this paper, the effect of different spatial extents in a state-of-the-art system for visual concept detection is investigated. Based on the observation that SIFT descriptors with different spatial extents yield large performance differences, we propose a concept detection system that combines feature representations for different spatial extents using multiple kernel learning. It is shown experimentally on a large set of 101 concepts from the Mediamill Challenge and on the PASCAL Visual Object Classes Challenge that these feature representations are complementary: Superior performance can be achieved on both test sets using the proposed system.