Texture analysis and classification remain as one of the biggest challenges for the field of computer vision and pattern recognition. This article presents a robust hybrid combination technique to build a combined classifier that is able to tackle the problem of classification of rotation-invariant 2D textures. Diversity in the components of the combined classifier is enforced through variation...

Information fusion has, in the form of multiple classifier systems, long been a successful tool in pattern recognition applications. It is also becoming increasingly popular in biomedical image analysis, for example in computer-aided diagnosis and in image segmentation. In this paper, we extend the principles of multiple classifier systems by considering information fusion of classifier inputs ...

We introduce a multiple classifier system which incorporates a genetic algorithm in order to simultaneously and dynamically select not only the participating classifiers but also the combination rule to be used. In this paper we focus on exploring the efficiency of such an evolutionary algorithm with respect to the behaviour of the resulting multiexpert configurations. To this end we initially ...

It is well-established in the pattern recognition community that the performance of classifiers can be greatly improved by combining the outputs of multiple classifiers. In this paper, we introduce the concept of adaptive performance-based classifier combination, i.e., the weighting of classifiers based on their estimated recognition performance, to generic object recognition. Using an expectat...

Most of what we know about multiple classifier systems is based on empirical findings, rather than theoretical results. Although there exist some theoretical results for simple and weighted averaging, it is difficult to gain an intuitive feel for classifier combination. In this paper we derive a bound on the region of the feature space in which the decision boundary can lie, for several methods...

Chemical Named Entity Recognition (NER) is the basic step for consequent information extraction tasks such as named entity resolution, drug-drug interaction discovery, extraction of the names of the molecules and their properties. Improvement in the performance of such systems may affects the quality of the subsequent tasks. Chemical text from which data for named entity recognition is extracte...

brain mr images tissue segmentation is one of the most important parts of the clinical diagnostic tools. pixel classification methods have been frequently used in the image segmentation with two supervised and unsupervised approaches up to now. supervised segmentation methods lead to high accuracy but they need a large amount of labeled data, which is hard, expensive and slow to obtain. moreove...

A multi-classifier system obtained by combining several individual classifiers usually exhibits a better performance (precision) than any of the original classifiers. In this work we use a multi-classifier based on a forest of randomly generated fuzzy decision trees (Fuzzy Random Forest), and we propose a new method to combine their decisions to obtain the final decision of the forest. The prop...

Classifier fusion may generate more accurate classification than each of the basic classifiers. Fusion is often based on fixed combination rules like the product, average etc. This paper presents decision templates as classifier fusion method for the recognition of the handwritten English and Farsi numerals (1-9). The process involves extracting a feature vector on well-known image databases. T...