Mohamed A. Khabou

Shape recognition using eigenvalues of the Dirichlet Laplacian

The eigenvalues of the Dirichlet Laplacian are used to generate three different sets of features for shape recognition and classification in binary images. The generated features are rotation-, translation-, and size-invariant. The features are also shown to be tolerant of noise and boundary deformation. These features are used to classify hand-drawn, synthetic, and natural shapes with correct classification rates ranging from 88.9% to 99.2%. The classification was done using few features (only two features in some cases) and simple feedforward neural networks or minimum Euclidian distance.

2IBGSOM: interior and irregular boundaries growing self-organizing maps

In this paper, we introduce a new variant of growing self-organizing maps (GSOM) based on Alahakoons algorithm for SOM training; so called 2IBGSOM (interior and irregular boundaries growing self-organizing maps). Its dynamically evolving structure for SOM, which allocates map size and shape during the unsupervised training process. 2IBGSOM starts with a small number of initial nodes and generates new nodes from the boundary and the interior of the network. 2IBGSOM represents the structure of the training data as accurately as possible. Our proposed method was tested on real world databases and showed better performance than the classical SOM and the growing grid (GG) algorithms. Three criteria were used to compare the above algorithms with our proposed method; the quantization error; the topological error and the labeling error to have more accuracy on the produced structure. Results report that 2IBGSOM shows a very good capacity of estimation for the training data based on the three tested factors.

An Iterative Method for Deciding SVM and Single Layer Neural Network Structures

We present two new classifiers for two-class classification problems using a new Beta-SVM kernel transformation and an iterative algorithm to concurrently select the support vectors for a support vector machine (SVM) and the hidden units for a single hidden layer neural network to achieve a better generalization performance. To construct the classifiers, the contributing data points are chosen on the basis of a thresholding scheme of the outputs of a single perceptron trained using all training data samples. The chosen support vectors are used to construct a new SVM classifier that we call Beta-SVN. The number of chosen support vectors is used to determine the structure of the hidden layer in a single hidden layer neural network that we call Beta-NN. The Beta-SVN and Beta-NN structures produced by our method outperformed other commonly used classifiers when tested on a 2-dimensional non-linearly separable data set.

An indexing and retrieval system of historic art images based on fuzzy shape similarity

We present an indexing and retrieval system of historic art images based on fuzzy shape similarity. The system is composed of three principal components: object annotation, object shape indexing, and query/retrieval. The object annotation in database images is done manually offline. The object shape indexing and retrieval, however, are done automatically. Annotated object shapes are indexed using an extended curvature scale space (CSS) descriptor suitable for concave and convex shapes. The query/retrieval of pertinent shapes from the database starts with a user drawing query (with a computer mouse or a pen) that is compared to entries in the database using a fuzzy similarity measure. The system is tested on a set of complex color and grey scale images of ancient documents, mosaics, and artifacts from the National Library of Tunisia, the National Archives of Tunisia, and a selection of Tunisian museums. The systems recall and precision rates were 83% and 60%, respectively.

A System for Historic Document Image Indexing and Retrieval Based on XML Database Conforming to MPEG7 Standard

We present a novel image indexing and retrieval system based on object contour description. Extended curvature scale space (CSS) descriptors composed of both local and global features are used to represent and index concave and convex object shapes. These features are size, rotation, and translation invariant. The index is saved into an XML database conforming to the MPEG7 standard. Our system contains a graphical user interface that allows a user to search a database using either sample or user-drawn shapes. The system was tested using two image databases: the Tunisian National Library (TNL) database containing 430 color and gray-scale images of historic documents, mosaics, and artifacts; and the Squid dataset containing 1100 contour images of fish. Recall and precision rates of 94% and 87%, respectively, were achieved on the TNL database and 71% and 86% on the Squid database. Average response time to a query is about 2.55 sec on a 2.66 GHz Pentium-based computer with 256 Mbyte of RAM.

AN INTELLIGENT DECISION-MAKING SYSTEM BASED ON MULTIPLE CLASSIFIERS UPDATED USING CONFIDENCE RATES AND STRESS PARAMETERS

We develop an intelligent multi-classifier decision-making system for multi-class classification tasks. The proposed system called I-MDS (Intelligent Multiple Decision System) uses a dynamic scheme to combine the information provided by the individual classifiers and make a classification decision. The individual classifiers in the system are interconnected and use a negotiation scheme to come up with a unified decision. During the interactive and reactive negotiation process, individual classifiers are allowed to revaluate their confidence in their individual decisions and to respond to a system-wise stress parameter that keeps increasing as long as the system does not reach a unified decision. If after a certain number of negotiation rounds the system can not reach a unified decision, the input pattern is rejected. The proposed systems were tested on multi-class classification problems from the UCI repository and were shown to produce better classification rates and fewer misclassifications than majority voting combination technique.

Texture and fuzzy colour features to index Roman mosaic-images

We present an efficient approach to index and retrieve Roman mosaic-images by region. The image regions are defined based on the mosaic-image presentation. For more efficiency, the proposed approach uses an index combining texture and fuzzy colour features. These features are fuzzy image dominant colour, fuzzy colour histogram and local Robert gradient binary pattern operator LRGBP. Similar images are extracted using a fuzzy similarity and histogram intersection measures. Our proposed index is tested on database containing 800 images of historical Roman mosaics from the 1st to 4th century. Tests demonstrate that the fusion of the fuzzy colour and texture features increases the precision of the retrieval system. Evaluation tests and comparison with several other prevailing approaches prove a remarkable performance of our proposed approach.

Classification and feature analysis of actigraphy signals

We evaluate the effectiveness of 63 different features commonly used in the classification of actigraphy signals. We implement two feature selection techniques to rank the effectiveness of the features and select the “best” among them. Once the “best” feature(s) is (are) selected, a minimum distance classifier is used to classify the actigraphy signals into different types of activity. The minimum distance classifier uses class prototypes generated using either k-means or max-min clustering algorithm. Correct classification rates of 95%-100% were achieved using only 1-5 features.

An Intelligent mutli-object retrieval system for historical mosaics

In this work we present a Mosaics Intelligent Retrieval System (MIRS) for digital museums. The objective of this work is to attain a semantic interpretation of images of historical mosaics. We use the fuzzy logic techniques and semantic similarity measure to extract knowledge from the images for multi-object indexing. The extracted knowledge provides the users (experts and laypersons) with an intuitive way to describe and to query the images in the database. Our contribution in this paper is firstly, to define semantic fuzzy linguistic terms to encode the object position and the inter-objects spatial relationships in the mosaic image. Secondly, to present a fuzzy color quantization approach using the human perceptual HSV color space and finally, to classify semantically the mosaics images using a semantic similarity measure. The automatically extracted knowledge are collected and traduced into XML language to create mosaics metadata. This system uses a simple Graphic User Interface (GUI) in natural language and applies the classification approach both on the mosaics images database and on user queries, to limit images classes in the retrieval process. MIRS is tested on images from the exceptional Tunisian collection of complex mosaics. Experimental results are based on queries of various complexities which yielded a systems recall and precision rates of 86.6% and 87.1%, respectively, while the classification approach gives an average success rate evaluated to 76%. Keywords—retrieval; mosaics; metadata; classification; multi- objects

Shape Recognition Based on Eigenvalues of the Laplacian

Abstract Recently there has been a surge in the use of the eigenvalues of linear operators in problems of pattern recognition. In this chapter, we discuss the theoretical, numerical, and experimental aspects of using four wellknown linear operators and their eigenvalues for shape recognition. In particular, the eigenvalues of the Laplacian operator under Dirichlet and Neumann boundary conditions, as well as those of the clamped plate and buckling of a clamped plate, are examined. Since the ratios of eigenvalues for each of these operators are translation, rotation, and scale invariant, four feature vectors are extracted for the purpose of shape recognition. These feature vectors are then fed into a basic neural network for training and measuring the performance of each of the feature vectors, which in turn were all shown to be reliable features for shape recognition. We also offer a review of the literature on finite difference schemes for these operators and summarize key facts about their eigenvalues that are of relevance in image recognition.