Feature selection with dynamic mutual information

Feature selection plays an important role in data mining and pattern recognition, especially for large scale data. During past years, various metrics have been proposed to measure the relevance between different features. Since mutual information is nonlinear and can effectively represent the dependencies of features, it is one of widely used measurements in feature selection. Just owing to these, many promising feature selection algorithms based on mutual information with different parameters have been developed. In this paper, at first a general criterion function about mutual information in feature selector is introduced, which can bring most information measurements in previous algorithms together. In traditional selectors, mutual information is estimated on the whole sampling space. This, however, cannot exactly represent the relevance among features. To cope with this problem, the second purpose of this paper is to propose a new feature selection algorithm based on dynamic mutual information, which is only estimated on unlabeled instances. To verify the effectiveness of our method, several experiments are carried out on sixteen UCI datasets using four typical classifiers. The experimental results indicate that our algorithm achieved better results than other methods in most cases.     

Automatic face detection in video sequences using local normalization and optimal adaptive correlation techniques

Automatic human face detection from video sequences is an important component of intelligent human computer interaction systems for video surveillance, face recognition, emotion recognition and face database management. This paper proposes an automatic and robust method to detect human faces from video sequences that combines feature extraction and face detection based on local normalization, Gabor wavelets transform and Adaboost algorithm. The key step and the main contribution of this work is the incorporation of a normalization technique based on local histograms with optimal adaptive correlation (OAC) technique to alleviate a common problem in conventional face detection methods: inconsistent performance due to sensitivity to variation illuminations such as local shadowing, noise and occlusion. The approach uses a cascade of classifiers to adopt a coarse-to-fine strategy for achieving higher detection rates with lower false positives. The experimental results demonstrate a significant performance improvement gains and achieved by local normalization over methods without normalizations in real video sequences with a wide range of facial variations in color, position, scale, and varying lighting conditions.     

Distortion-free secret image sharing mechanism using modulus operator

The (t, n)-threshold has been extended to secret image sharing due to its practicability. In this article, we provide a novel version that employs the modulus operator to embed the secret share into a host image. The simulator shows that the modulus operator is useful for decreasing shadow image distortion. Using Rabin's signature cryptosystem, participants can detect if a cheater exists in the cooperation. In particular, the new mechanism permits involved members to restore a lossless secret image and to reconstruct a distortion-free host image.     

Enzymatic competition: Modeling and verification with timed hybrid petri nets

The formalism of hybrid functional petri nets (HFPN) has proved its convenience for simulating biological systems. The drawback of the noticeable expressiveness of HFPN is the difficulty to perform formal verifications of dynamical properties. In this article, we propose a model-checking procedure for timed hybrid petri nets (THPN), a sub-class of HFPN. This procedure is based on the translation of the THPN model and of the studied property into real-time automata. It is applied to model enzymatic competitions existing in amphibian metamorphosis.     

Text line and word segmentation of handwritten documents

In this paper, we present a segmentation methodology of handwritten documents in their distinct entities, namely, text lines and words. Text line segmentation is achieved by applying Hough transform on a subset of the document image connected components. A post-processing step includes the correction of possible false alarms, the detection of text lines that Hough transform failed to create and finally the efficient separation of vertically connected characters using a novel method based on skeletonization. Word segmentation is addressed as a two class problem. The distances between adjacent overlapped components in a text line are calculated using the combination of two distance metrics and each of them is categorized either as an inter- or an intra-word distance in a Gaussian mixture modeling framework. The performance of the proposed methodology is based on a consistent and concrete evaluation methodology that uses suitable performance measures in order to compare the text line segmentation and word segmentation results against the corresponding ground truth annotation. The efficiency of the proposed methodology is demonstrated by experimentation conducted on two different datasets: (a) on the test set of the ICDAR2007 handwriting segmentation competition and (b) on a set of historical handwritten documents.     

Synthetic handwritten CAPTCHAs

CAPTCHAs (completely automated public Turing test to tell computers and humans apart) are in common use today as a method for performing automated human verification online. The most popular type of CAPTCHA is the text recognition variety. However, many of the existing printed text CAPTCHAs have been broken by web-bots and are hence vulnerable to attack. We present an approach to use human-like handwriting for designing CAPTCHAs. A synthetic handwriting generation method is presented, where the generated textlines need to be as close as possible to human handwriting without being writer-specific. Such handwritten CAPTCHAs exploit the differential in handwriting reading proficiency between humans and machines. Test results show that when the generated textlines are further obfuscated with a set of deformations, machine recognition rates decrease considerably, compared to prior work, while human recognition rates remain the same.     

Tangential cover for thick digital curves

The recognition of digital shapes is a deeply studied problem. The arithmetical framework, initiated by Reveillès [Géométrie discrète, calcul en nombres entiers et algorithmique, Thèse d’Etat, 1991], provides a powerful theoretical basis, as well as many algorithms to deal with digital objects. The tangential cover, first presented in Feschet and Tougne [Optimal time computation of the tangent of a discrete curve: application to the curvature, in: G. Bertrand, M. Couprie, L. Perroton (Eds.), 8th Discrete Geometry for Computer Imagery, Lecture Notes in Computer Science, vol. 1568, Springer, Berlin, 1999, pp. 31-40] and Feschet [Canonical representations of discrete curves, Pattern Anal. Appl. 8(1-2) (2005) 84-94] is a useful tool for representing geometric digital primitives. It computes the set of all maximal segments of a digital curve and permits either to obtain minimal length polygonalization or asymptotic convergence of tangents estimations. Nevertheless, the arithmetical approach does not tolerate the introduction of irregularities, which are however inherent to the acquisition of digital shapes. The present paper is an extension of Faure and Feschet [Tangential cover for thick digital curves, in: D. Coeurjolly, I. Sivignon, L. Tougne, F. Dupont (Eds.), DGCI 2008, Lecture Notes in Computer Science, vol. 4992, Springer, Berlin, 2008, pp. 358-369], in which we propose a new definition for a class of the so-called “thick digital curves” that applies well to a large class of digital object boundaries. We then propose an extension of the tangential cover to thick digital curves and provide an algorithm with an O(nlogn) time complexity, where n denotes the number of points of specific subparts of the thick digital curve. In order to keep up with this low complexity, some critical points must be taken into account. We describe all required implementation details in this paper.     

Gaussian kernel optimization for pattern classification

This paper presents a novel algorithm to optimize the Gaussian kernel for pattern classification tasks, where it is desirable to have well-separated samples in the kernel feature space. We propose to optimize the Gaussian kernel parameters by maximizing a classical class separability criterion, and the problem is solved through a quasi-Newton algorithm by making use of a recently proposed decomposition of the objective criterion. The proposed method is evaluated on five data sets with two kernel-based learning algorithms. The experimental results indicate that it achieves the best overall classification performance, compared with three competing solutions. In particular, the proposed method provides a valuable kernel optimization solution in the severe small sample size scenario.     

A genetic algorithm with gene rearrangement for K-means clustering

In this paper, a new clustering algorithm based on genetic algorithm (GA) with gene rearrangement (GAGR) is proposed, which in application may effectively remove the degeneracy for the purpose of a more efficient search. A new crossover operator that exploits a measure of similarity between chromosomes in a population is also presented. Adaptive probabilities of crossover and mutation are employed to prevent the convergence of the GAGR to a local optimum. Using the real-world data sets, we compare the performance of our GAGR clustering algorithm with K-means algorithm and other GA methods. An application of the GAGR clustering algorithm in unsupervised classification of multispectral remote sensing images is also provided. Experiment results demonstrate that the GAGR clustering algorithm has high performance, effectiveness and flexibility.