Decision Combination in Multiple Classifier Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Improved boosting algorithms using confidence-rated predictions
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Learning and inferring a semantic space from user's relevance feedback for image retrieval
Proceedings of the tenth ACM international conference on Multimedia
Improved Pairwise Coupling Classification with Correcting Classifiers
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Context and configuration-based scene classification
Context and configuration-based scene classification
Automatic Linguistic Indexing of Pictures by a Statistical Modeling Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
Reducing multiclass to binary: a unifying approach for margin classifiers
The Journal of Machine Learning Research
A Two-Stage Classifier for Broken and Blurred Digits in Forms
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 2 - Volume 2
A study of the behavior of several methods for balancing machine learning training data
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
Data Mining on Imbalanced Data Sets
ICACTE '08 Proceedings of the 2008 International Conference on Advanced Computer Theory and Engineering
SMOTE: synthetic minority over-sampling technique
Journal of Artificial Intelligence Research
Solving multiclass learning problems via error-correcting output codes
Journal of Artificial Intelligence Research
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It is very much essential for the multimedia information organization to provide accurate and scalable solutions to map low-level perceptual features to high-level semantics. Therefore automatic and efficient annotation of images is needed for rapid content based retrieval and indexing; it alleviates the disadvantage of any manual annotation. The proposed system for pattern matching and annotation from large image databases has been given based on the combination of Fractal Transform and gentle AdaBoost algorithm. This technique involves two main stages in classification phase wherein first, we make use of gentle AdaBoost algorithm as it is best suited for object detection task and also has lower computational complexity. Next, a mathematical representation is associated to the images of the database, this representation is a set of function parameters resulting from a dedicated fractal interpolation scheme, and used as an index by a retrieval algorithm. Proposed algorithm works completely in the Fractal transform parameter space of both images and patterns, to obtain performances well-matched with an interactive search. In this paper, we also try to overcome the orientation, scaling and class imbalance problem in image annotation by choosing an over sampling method for learning the classifier. Experimental results of IFSMOTE shows higher prediction quality, and performs better than the classical SVM, SMOTE and FSMOTE.