Radhouane Guermazi

Violent web images classification based on MPEG7 color descriptors

In this article, we present a contribution to the violent Web images classification. This subject is deeply important as it has a potential use for many applications such as violent Web sites filtering. We propose to combine the techniques of image analysis and data-mining to relate low level characteristics extracted from the images colors to a higher characteristic of violence which could be contained in the image. We present a comparative study of different data mining techniques to classify violent Web images. Also, we discuss how the combination learning based methods can improve accuracy rate. Our results show that our approach can detect violent content effectively.

Adapted pruning scheme for the framework of imbalanced data-sets

Abstract Learning from imbalanced data is attracting an increasing interest by the machine learning community. This is mainly due to the high number of real applications that are affected by this situation. The adaptation of the standard decision trees to deal with imbalanced data represents one of the important number of approaches that have been developed to address this problem. This adaptation has been proposed under three different perspectives: splitting criterion, assignment rule and pruning. In this paper, we focus our attention to the pruning of decision trees. We propose an adaptation of the standard pruning algorithm MCCP to address the skewed-data problem. Our contribution affects two levels: adaption of the metric used in selecting nodes to be firstly pruned and change of the evaluation measure used in selecting the best decision-tree through the pruning set. Our goal is to show that, contrary to the popular belief in the literature enquiring into the uselessness of decision tree pruning, an adaptive pruning technique for imbalanced situations is more efficient and more accurate towards the minority class. A total of twelve binary class data-sets having different imbalance ratio are used to test the performance of the proposed method. Experimental results show that the proposed post-pruning approach can increase the performance of imbalanced decision trees in terms of evaluation measures that are recent and appropriate for the context of imbalanced classification.

Classification of violent web images using context based analysis

The development of the Web has been paralleled by the proliferation of a harmful content on its pages. Using Violent Web images as a case study, we tend to present a novel approach to their classification. This subject is of high importance as it has a potential use in many applications such as violent Web sites filtering. We, therefore, focus our attention on the extraction of contextual image features from the Web page. Also, we present a comparative study of different data mining techniques to classify violent Web images. The results we achieved show that our approach can detect violent content effectively.

Automatic violent content web filtering approach based on the KDD process

Purpose – The growth of the web and the increasing number of documents electronically available has been paralleled by the emergence of harmful web pages content such as pornography, violence, racism, etc. This emergence involved the necessity of providing filtering systems designed to secure the internet access. Most of them process mainly the adult content and focus on blocking pornography, marginalizing violence. The purpose of this paper is to propose a violent web content detection and filtering system, which uses textual and structural content‐based analysis.Design/methodology/approach – The violent web content detection and filtering system uses textual and structural content‐based analysis based on a violent keyword dictionary. The paper focuses on the keyword dictionary preparation, and presents a comparative study of different data mining techniques to block violent content web pages.Findings – The solution presented in this paper showed its effectiveness by scoring a 89 per cent classification ...

Facial-expression recognition based on a low-dimensional temporal feature space

This paper suggests a facial-expression recognition in accordance with face video sequences based on a newly low-dimensional feature space proposed. Indeed, we extract a Pyramid of uniform Temporal Local Binary Pattern representation, using only XT and YT orthogonal planes (PTLBPu2). Then, a Wrapper method is applied to select the most discriminating sub-regions, and therefore, reduce the feature space that is going to be projected on a low-dimensional feature space by applying the Principal Component Analysis (PCA). Support Vector Machine (SVM) and C4.5 algorithm have been tested for the classification of facial expressions. Experiments conducted on CK + and MMI, which are the two famous facial-expression databases, have shown the effectiveness of the approach proposed under a lab-controlled environment with more than 97% of recognition rate as well as under an uncontrolled environment with more than 92%.

AECID: Asymmetric entropy for classifying imbalanced data

Abstract In class imbalance problems, it is often more important and expensive to recognize examples from the minority class than from the majority. Standard entropies are known to exhibit poor performance towards the rare class since they take their maximal value for the uniform distribution. To deal with this issue, the present paper introduced a novel adaption of the decision-tree algorithm to imbalanced data situations. We focused, more specifically, on how to let the split criterion discriminate the minority-class examples on a binary-classification problem. Our algorithm uses a new asymmetric entropy measure, termed AECID, which adjusts the most uncertain class distribution to the prior class distribution and includes it in the evaluation of a node impurity. Unlike most competitive split, which include only the prior imbalanced class distribution in their formula, the proposed entropy is customizable with an adjustable concavity to take into account the specificities of each data-set and to better comply with the users’ requirements. Extensive experiments were conducted on thirty-six real life imbalanced data-sets to apprise the effectiveness of the proposed approach. Furthermore, the comparative results prove that the new proposal outperforms various algorithmic, data level and ensemble approaches that have been already proposed for imbalanced learning.

Facial Expression Recognition Improvement through an Appearance Features Combination.

This paper suggests an approach to automatic facial expression recognition for images of frontal faces. Two methods of appearance features extraction is combined: Local Binary Pattern (LBP) on the whole face region and Eigenfaces on the eyes-eyebrows and/or on the mouth regions. Support Vector Machines (SVM), K Nearest Neighbors (KNN) and MultiLayer Perceptron (MLP) are applied separately as learning technique to generate classifiers for facial expression recognition. Furthermore, we conduct to the many empirical studies to fix the optimal parameters of the approach. We use three baseline databases to validate our approach in which we record interesting results compared to the related works regardless of using faces under controlled and uncontrolled environment.