Alessia Amelio

Language discrimination by texture analysis of the image corresponding to the text

The manuscript provides a novel method for language identification using the texture analysis of the script. The method consists of mapping each letter from the text with certain script type. It is made according to characteristics concerning the position of the letter in the baseline area. In order to extract features, the co-occurrence matrix is computed. Then, the texture features are calculated. Extracted measures show meaningful differences due to dissimilarities in the script and language characteristics. It represents a basis in a decision-making process of the language identification. Feature classification is performed by the extension of a state-of-the-art method called genetic algorithms image clustering for document analysis. The proposed method is tested on an example of documents given in English, French, Slovenian and Serbian languages and compared to other well-known classification methods and feature representations in the state of the art. The results of experiments show the superiority of the proposed approach.

A New Evolutionary-Based Clustering Framework for Image Databases

A new framework to cluster images based on Genetic Algorithms (GAs) is proposed. The image database is represented as a weighted graph where nodes correspond to images and an edge between two images exists if they are sufficiently similar. The edge weight expresses the level of similarity of the feature vectors, describing color and texture content, associated with images. The image graph is then clustered by applying a genetic algorithm that divides it in groups of nodes connected by many edges with high weight, by employing as fitness function the concept of weighted modularity. Results on a well-known image database show that the genetic approach is able to find a partitioning in groups of effectively similar images.

Community Detection in Multidimensional Networks

The paper proposes a new approach to detect shared community structure in multidimensional networks based on the combination of multiobjective genetic algorithms, local search, and the concept of temporal smoothness, coming from evolutionary clustering. A multidimensional network is clustered by running on each slice a multiobjective genetic algorithm that maximizes the modularity on such a slice and, at the same time, minimizes the difference between the community structure obtained for the current layer and that found on the already considered dimensions. Experiments on synthetic and real-world datasets show the ability of the approach in discovering latent shared clustering of objects.

Clustering documents in evolving languages by image texture analysis

This paper introduces a new method for clustering of documents, which have been written in a language evolving during different historical periods, with an example of the Italian language. In the first phase, the text is transformed into a string of four numerical codes, which have been derived from the energy profile of each letter, defining the height of the letters and their location in the text line. Each code represents a gray level and the text is codified as a 1-D image. In the second phase, texture features are extracted from the obtained image in order to create document feature vectors. Subsequently, a new clustering algorithm is employed on the feature vectors to discriminate documents from different historical periods of the language. Experiments are performed on a database of Italian documents given in Italian Vulgar and modern Italian. Results demonstrate that this proposed method perfectly identifies the historical periods of the language of the documents, outperforming other well-known clustering algorithms generally adopted for document categorization and other state-of-the-art text-based language models.

Identification of Fraktur and Latin Scripts in German Historical Documents Using Image Texture Analysis

ABSTRACT This article proposes an algorithm for script identification by textural analysis of the image corresponding to the script types. In the first phase, each letter is modeled by the equivalent script type, which is determined by its position in the baseline area. Then, feature extraction is carried out. It is based on the script type cooccurrence pattern analysis. The obtained features of the script are stored for further analysis. The difference in script characteristics contributes to the diversity of the extracted features, which simplify the feature classification obtained by an extension of a state-of-the-art classification tool called Genetic Algorithms Image Clustering for Document Analysis. Accordingly, it represents the key element in the decision-making process of script identification. The proposed method is tested on an example of German printed documents, which contain Latin and Fraktur scripts. The experiment shows correct results, which is promising.

Classification of the Extremely Low Frequency Magnetic Field Radiation Measurement from the Laptop Computers

Abstract The paper considers the level of the extremely low-frequency magnetic field, which is produced by laptop computers. The magnetic field, which is characterized by extremely low frequencies up to 300 Hz is measured due to its hazardous effects to the laptop users health. The experiment consists of testing 13 different laptop computers in normal operation conditions. The measuring of the magnetic field is performed in the adjacent neighborhood of the laptop computers. The measured data are presented and then classified. The classification is performed by the K-Medians method in order to determine the critical positions of the laptop. At the end, the measured magnetic field values are compared with the critical values suggested by different safety standards. It is shown that some of the laptop computers emit a very strong magnetic field. Hence, they must be used with extreme caution.

A genetic algorithm for color image segmentation

A genetic algorithm for color image segmentation is proposed. The method represents an image as a weighted undirected graph, where nodes correspond to pixels, and edges connect similar pixels. Similarity between two pixels is computed by taking into account not only brightness, but also color and texture content. Experiments on images from the Berkeley Image Segmentation Dataset show that the method is able to partition natural and human scenes in a number of regions consistent with human visual perception. A quantitative evaluation of the method compared with other approaches shows that the genetic algorithm can be very competitive in partitioning color images.

An approach to the language discrimination in different scripts using adjacent local binary pattern

The paper proposes a language discrimination method of documents. First, each letter is encoded with the certain script type according to its status in baseline area. Such a cipher text is subjected to a feature extraction process. Accordingly, the local binary pattern as well as its expanded version called adjacent local binary pattern are extracted. Because of the difference in the language characteristics, the above analysis shows significant diversity. This type of diversity is a key aspect in the decision-making differentiation of the languages. Proposed method is tested on an example of documents. The experiments give encouraging results.

Characterization and Distinction Between Closely Related South Slavic Languages on the Example of Serbian and Croatian

The paper proposes a new method for characterization and distinction between closely related languages on the example of Serbian and Croatian languages. In the first step, the method transforms the text in different languages into the uniformly coded text. It is carried out in accordance to the position of each sign of the script in the text line and its height. Then, the coded text given as 1-D image is subjected to the texture analysis. According to that analysis, a feature vector of 28 elements is established. These 28 elements are extracted from co-occurrence texture and adjacent local binary pattern analysis. The feature vector is a starting point for classification by an extension of a state of the art method, called GA-ICDA. As a result, the distinction between the closely related languages is correctly accomplished. The method is tested on a database of documents in Serbian and Croatian languages. The experiments give promising results.

Is Normalized Mutual Information a Fair Measure for Comparing Community Detection Methods

Normalized mutual information (NMI) is a widely used measure to compare community detection methods. Recently, however, the need of adjustment for information theoretic based measures has been argued because of their tendency in choosing clustering solutions with more communities. In this paper an experimental evaluation is performed to investigate this problem, and an adjustment that scales the values of NMI is proposed. Experiments on synthetic generated networks highlight the unbiased behavior of scaled NMI.