Sum and Difference Histograms for Texture Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
A training algorithm for optimal margin classifiers
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
IEEE Transactions on Pattern Analysis and Machine Intelligence
International Journal of Computer Vision
Decision Combination in Multiple Classifier Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine Learning
Comparing images using color coherence vectors
MULTIMEDIA '96 Proceedings of the fourth ACM international conference on Multimedia
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Machine Learning
A compact and efficient image retrieval approach based on border/interior pixel classification
Proceedings of the eleventh international conference on Information and knowledge management
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Image Indexing Using Color Correlograms
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 12 - Volume 12
International Journal of Computer Vision
Handbook of Multibiometrics (International Series on Biometrics)
Handbook of Multibiometrics (International Series on Biometrics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Information fusion for wireless sensor networks: Methods, models, and classifications
ACM Computing Surveys (CSUR)
Classifier ensembles: Select real-world applications
Information Fusion
Wavelet-based fingerprint image retrieval
Journal of Computational and Applied Mathematics
A brief introduction to boosting
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
Automatic fruit and vegetable classification from images
Computers and Electronics in Agriculture
Artificial Intelligence Review
Fast and Scalable Local Kernel Machines
The Journal of Machine Learning Research
What does classifying more than 10,000 image categories tell us?
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)
Exploiting contextual spaces for image re-ranking and rank aggregation
Proceedings of the 1st ACM International Conference on Multimedia Retrieval
Image re-ranking and rank aggregation based on similarity of ranked lists
CAIP'11 Proceedings of the 14th international conference on Computer analysis of images and patterns - Volume Part I
Classification by cluster analysis: a new meta-learning based approach
MCS'11 Proceedings of the 10th international conference on Multiple classifier systems
Evaluating feature combination in object classification
ISVC'11 Proceedings of the 7th international conference on Advances in visual computing - Volume Part II
Computers and Electrical Engineering
Comparative study of global color and texture descriptors for web image retrieval
Journal of Visual Communication and Image Representation
Large-scale image classification: Fast feature extraction and SVM training
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Parallel consensual neural networks
IEEE Transactions on Neural Networks
New results on error correcting output codes of kernel machines
IEEE Transactions on Neural Networks
Large-scale image classification with trace-norm regularization
CVPR '12 Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Discriminative feature fusion for image classification
CVPR '12 Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Automatic Tracking of Indoor Soccer Players Using Videos from Multiple Cameras
SIBGRAPI '12 Proceedings of the 2012 25th SIBGRAPI Conference on Graphics, Patterns and Images
Automatic Classifier Fusion for Produce Recognition
SIBGRAPI '12 Proceedings of the 2012 25th SIBGRAPI Conference on Graphics, Patterns and Images
Improving Image Classification through Descriptor Combination
SIBGRAPI '12 Proceedings of the 2012 25th SIBGRAPI Conference on Graphics, Patterns and Images
Reducing overfitting of AdaBoost by clustering-based pruning of hard examples
Proceedings of the 7th International Conference on Ubiquitous Information Management and Communication
Hi-index | 0.10 |
The frequent growth of visual data, either by countless monitoring video cameras wherever we go or the popularization of mobile devices that allow each person to create and edit their own images and videos have contributed enormously to the so-called ''big-data revolution''. This shear amount of visual data gives rise to a Pandora box of new visual classification problems never imagined before. Image and video classification tasks have been inserted in different and complex applications and the use of machine learning-based solutions has become the most popular approach for several applications. Notwithstanding, there is no silver bullet that solves all the problems, i.e., it is not possible to characterize all images of different domains with the same description method nor is it possible to use the same learning method to achieve good results in any kind of application. In this work, we aim at proposing a framework for classifier selection and fusion. Our method seeks to combine image characterization and learning methods by means of a meta-learning approach responsible for assessing which methods contribute more towards the solution of a given problem. The framework uses a strategy of classifier selection which pinpoints the less correlated, yet effective, classifiers through a series of diversity measures analysis. The experiments show that the proposed approach achieves comparable results to well-known algorithms from the literature on four different applications but using less learning and description methods as well as not incurring in the curse of dimensionality and normalization problems common to some fusion techniques. Furthermore, our approach is able to achieve effective classification results using very reduced training sets. The proposed method is also amenable to continuous learning and flexible enough for implementation in highly-parallel architectures.