Claudio De Stefano

Combining Single Class Features for Improving Performance of a Two Stage Classifier

We propose a feature selection--based approach for improving classification performance of a two stage classification system in contexts where a high number of features is involved. A problem with a set of

A SALIENCY-BASED SEGMENTATION METHOD FOR ONLINE CURSIVE HANDWRITING

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A hybrid evolutionary algorithm for bayesian networks learning: an application to classifier combination

classes is subdivided into a set of

CLASSIFIER COMBINATION BY BAYESIAN NETWORKS FOR HANDWRITING RECOGNITION

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Writing Order Recovery from Off-Line Handwriting by Graph Traversal

two class problems. In each problem, a GA--based feature selection algorithm is used for finding the best subset of features. These subsets are then used for training

A Weighted Majority Vote Strategy Using Bayesian Networks

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Reading Cursive Handwriting

classifiers. In the classification phase, unknown samples are given in input to each of the trained classifiers by using the corresponding subspace. In case of conflicting responses, the sample is sent to a suitably trained supplementary classifier. The proposed approach has been tested on a real world dataset containing hyper--spectral image data. The results favourably compare with those obtained by other methods on the same data.

Learning Bayesian Networks by Evolution for Classifier Combination

We propose a model for the segmentation of cursive handwriting into strokes that has been derived in analogy with those proposed in the literature for early processing tasks in primate visual system. The model allows reformulating the problem of selecting on the ink the points corresponding to perceptually relevant changes of curvature as a preattentive, purely bottom-up visual task, where the conspicuity of curvature changes is measured in terms of their saliency. The modeling of the segmentation as a saliency-driven visual task has lead to a segmentation algorithm whose architecture is biologically-plausible and that does not rely on any parameter other than those that can be directly obtained from the ink. Experimental results show that the performance is very stable and predictable, thus preventing those erratic behaviors of segmentation methods often reported in the literature. They also suggest that the proposed measure of saliency has a direct relation with the dynamics of the handwriting, so as it could be used to capture in a quantitative way some aspects of cursive handwriting intuitively related to the notion of style.

An Adaptive Reject Option for LVQ Classifiers

Classifier combination methods have shown their effectiveness in a number of applications. Nonetheless, using simultaneously multiple classifiers may result in some cases in a reduction of the overall performance, since the responses provided by some of the experts may generate consensus on a wrong decision even if other experts provided the correct one. To reduce these undesired effects, in a previous paper, we proposed a combining method based on the use of a Bayesian Network. The structure of the Bayesian Network was learned by using an Evolutionary Algorithm which uses a specifically devised data structure to encode Direct Acyclic Graphs. In this paper we presents a further improvement along this direction, in that we have developed a new hybrid evolutionary algorithm in which the exploration of the search space has been improved by using a measure of the statistical dependencies among the experts. Moreover, new genetic operators have been defined that allow a more effective exploitation of the solutions in the evolving population. The experimental results, obtained by using two standard databases, confirmed the effectiveness of the method.

Rejecting Both Segmentation and Classification Errors in Handwritten Form Processing

In the field of handwriting recognition, classifier combination received much more interest than the study of powerful individual classifiers. This is mainly due to the enormous variability among t...