Angelo Marcelli

Where Are the Niches? Dynamic Fitness Sharing

The problem of locating all the optima within a multimodal fitness landscape has been widely addressed in evolutionary computation, and many solutions, based on a large variety of different techniques, have been proposed in the literature. Among them, fitness sharing (FS) is probably the best known and the most widely used. The main criticisms to FS concern both the lack of an explicit mechanism for identifying or providing any information about the location of the peaks in the fitness landscape, and the definition of species implicitly assumed by FS. We present a mechanism of FS, i.e., dynamic fitness sharing, which has been devised in order to overcome these limitations. The proposed method allows an explicit, dynamic identification of the species discovered at each generation, their localization on the fitness landscape, the application of the sharing mechanism to each species separately, and a species elitist strategy. The proposed method has been tested on a set of standard functions largely adopted in the literature to assess the performance of evolutionary algorithms on multimodal functions. Experimental results confirm that our method performs significantly better than FS and other methods proposed in the literature without requiring any further assumption on the fitness landscape than those assumed by the FS itself.

Bayesian Integration of Face and Low-Level Cues for Foveated Video Coding

We present a Bayesian model that allows to automatically generate flxations/foveations and that can be suitably exploited for compression purposes. The twofold aim of this work is to investigate how the exploitation of high-level perceptual cues provided by human faces occurring in the video can enhance the compression process without reducing the perceived quality of the video and to validate such assumption with an extensive and principled experimental protocol. To such end, the model integrates top-down and bottom-up cues to choose the fixation point on a video frame: at the highest level, a fixation is driven by prior information and by relevant objects, namely human faces, within the scene; at the same time, local saliency together with novel and abrupt visual events contribute by triggering lower level control. The performance of the resulting video compression system has been evaluated with respect to both the perceived quality of foveated video clips and the compression gain with an extensive evaluation campaign, which has eventually involved 200 subjects.

ICDAR 2013 Competitions on Signature Verification and Writer Identification for On- and Offline Skilled Forgeries (SigWiComp 2013)

This paper presents the results of the ICDAR2013 competitions on signature verification and writer identification for on- and offline skilled forgeries jointly organized by PR researchers and Forensic Handwriting Examiners (FHEs). The aim is to bridge the gap between recent technological developments and forensic casework. Two modalities (signatures, and handwritten text) are considered where training and evaluation data (in Dutch and Japanese) were collected and provided by FHEs and PR-researchers. Four tasks were defined where the systems had to perform Dutch offline signature verification, Japanese offline signature verification, Japanese online signature verification, and Dutch writer identification. The participants of the signatures modality were motivated to report their results in Likelihood Ratios (LR). This has made the systems even more interesting for application in forensic casework. For evaluation of signatures modality, we used both the traditional Equal Error Rate (EER) and forensically substantial Cost of Log Likelihood Ratios (Ĉllr). The system having the smallest value of the Minimum Cost of Log Likelihood Ratio (Ĉllrmin) is declared winner. For evaluation of the handwritten text modality, we used the precision and accuracy measures and winners are announced on the basis of best F-measure value.

Character preclassification based on genetic programming

This paper presents a learning system that uses genetic programming as a tool for automatically inferring the set of classification rules to be used during a preclassification stage by a hierarchical handwritten character recognition system. Starting from a structural description of the character shape, the aim of the learning system is that of producing a set of classification rules able to capture the similarities among those shapes, independently of whether they represent characters belonging to the same class or to different ones. In particular, the paper illustrates the structure of the classification rules, the grammar used to generate them and the genetic operators devised to manipulate the set of rules, as well as the fitness function used to drive the inference process. The experimental results obtained by using a set of 10,000 digits extracted from the NIST database show that the proposed preclassification is efficient and accurate, because it provides at most 6 classes for more than 87% of the samples, and the error rate almost equals the intrinsic confusion found in the data set. � 2002 Published by Elsevier Science B.V.

An adaptive weighted majority vote rule for combining multiple classifiers

We introduce a novel multiple classifier system that incorporates a global optimization technique based on a genetic algorithm for configuring the system. The system adopts the weighted majority vote approach to combine the decision of the experts, and obtains the weights by maximizing the performance of the whole set of experts, rather than that of each of them separately. The system has been tested on a handwritten digit recognition problem, and its performance compared with those exhibited by a system using the weights obtained during the training of each expert separately. The results of a set of experiments conducted on 30,000 digits extracted from the NIST database have shown that the proposed system exhibits better performance than those of the alternative one, and that such an improvement is due to a better estimate of the reliability of the participating classifiers.

Exploiting reliability for dynamic selection of classi .ers by means of genetic algorithms

We introduce a multiple classifier systemthat incorporates a global optimization technique based ona Genetic Algorithm for dynamically selecting the set ofexperts to use in the majority vote approach. The proposedtechnique is applicable when the experts in the pool provideboth the class assigned to the input sample and a measureof the reliability of the this classification. For each sample,the experts selected for participating in the majority voteare those whose reliability is larger than a given threshold.There are as many thresholds as the number of experts bythe number of classes. The values of the thresholds aimedat selecting the best set of experts for each input sampleare determined by a canonical Genetic Algorithm. Thereliability measures provided by the experts of the pool arealso used to implement the tie-break mechanism neededwithin the majority vote scheme. The system has beentested on a handwritten digit recognition problem, and itsperformance compared with those exhibited by other multi-expertsystems exploiting different combining rules.

A SALIENCY-BASED SEGMENTATION METHOD FOR ONLINE CURSIVE HANDWRITING

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.

A saliency-based multiscale method for on-line cursive handwriting shape description

We propose a method derived from an analogy with the primate visual system for selecting the best scale at which the electronic ink of the handwriting should be described. According to this analogy, the method computes a multiscale features maps by evaluating the curvature along the ink at different levels of resolution and arranges them into a pyramidal structure. Then, feature values extracted at different scales are combined in such a way that values that locally stand out from their surrounds are enhanced, while those comparable with their neighbours are suppressed. A saliency map is eventually obtained by combining those features value across all possible scales. Such a map is then used to select a representation that is largely invariant with respect to the shape variations encountered in handwriting. Experiments on two data sets have shown that simple algorithms adopting the proposed representation lead to very stable stroke segmentation and feature matching.

Handwritten numeral recognition by means of evolutionary algorithms

We present a handwritten numeral recognition system centered on a novel method for extracting the set of prototypes to be used during the classification. The method is based on an evolutionary learning mechanism that exploits a genetic algorithm with niching for producing the best set of prototypes. By combining the search power of genetic algorithms and the ability of niching mechanisms to maintain different prototypes during the evolution, the proposed method allows to obtain as many prototypes as needed to model the variability exhibited by the samples belonging to each class. Such a learning mechanism overcomes the limitations of other evolutionary learning methods proposed in the literature for dealing with problems characterized by a large amount of variability in the data set as in the case of handwriting recognition. Experiments have proved that the performance of the system is comparable with, or even better than that exhibited by a neural classifier.

An Alternative Approach to the Performance Evaluation of Thinning Algorithms for Document Processing Applications

It is proposed that the performance of thinning algorithms be evaluated with reference to a task which is especially relevant in connection with the use of these algorithms in the application domain of document processing: decomposition of digital lines into meaningful parts. The stability of the decompositions obtained according to simple rules, within given classes of lines, is assumed as a performance index. Experimental results, obtained using the ETL1 database of handprinted characters, are presented, to demonstrate the representativeness of the considered parameter.