The Strength of Weak Learnability
Machine Learning
Boosting a weak learning algorithm by majority
Information and Computation
Machine Learning
The Random Subspace Method for Constructing Decision Forests
IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine Learning
Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope
International Journal of Computer Vision
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
International Journal of Computer Vision
Sharing Visual Features for Multiclass and Multiview Object Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Speeded-Up Robust Features (SURF)
Computer Vision and Image Understanding
Universal and Adapted Vocabularies for Generic Visual Categorization
IEEE Transactions on Pattern Analysis and Machine Intelligence
Kernel Codebooks for Scene Categorization
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part III
International Journal of Computer Vision
Solving multiclass learning problems via error-correcting output codes
Journal of Artificial Intelligence Research
The visual concept detection task in ImageCLEF 2008
CLEF'08 Proceedings of the 9th Cross-language evaluation forum conference on Evaluating systems for multilingual and multimodal information access
Overview of the CLEF 2009 large-scale visual concept detection and annotation task
CLEF'09 Proceedings of the 10th international conference on Cross-language evaluation forum: multimedia experiments
Learning Midlevel Image Features for Natural Scene and Texture Classification
IEEE Transactions on Circuits and Systems for Video Technology
Nonparametric estimation of fisher vectors to aggregate image descriptors
ACIVS'11 Proceedings of the 13th international conference on Advanced concepts for intelligent vision systems
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Visual concept detection consists in assigning labels to an image or keyframe based on its semantic content. Visual concepts are usually learned from an annotated image or video database with a machine learning algorithm, posing this problem as a multiclass supervised learning task. Some practical issues appear when the number of concept grows, in particular in terms of available memory and computing time, both for learning and testing. To cope with these issues, we propose to use a multiclass boosting algorithm with feature sharing and reduce its computational complexity with a set of efficient improvements. For this purpose, we explore a limited part of the possible parameter space, by adequately injecting randomness into the crucial steps of our algorithm. This makes our algorithm able to handle a problem of classification with many classes in a reasonable time, thanks to a linear complexity with regards to the number of concepts considered as well as the number of feature and their size. The relevance of our algorithm is evaluated in the context of information retrieval, on the benchmark proposed into the ImageCLEF international evaluation campaign and shows competitive results.