Chokri Ben Amar

Beta wavelets. Synthesis and application to lossy image compression

Wavelets are known to have many connections to several other parts of mathematics, notably phase-space analysis of signal processing, reproducing kernel Hilbert spaces, coherent states in quantum mechanics, spline approximation theory, windowed Fourier transforms, filter banks and image analysis. In this paper, we study a new orthogonal mother wavelet and wavelet basis system based on Beta function as well as its derivatives. The most important conditions of mother wavelets to be satisfied are the admissibility, the regularity and the orthogonality. All these conditions were verified in the case of the proposed Beta wavelets family. Compared to most known wavelets as Haar, Daubechies, and Coifflet ones, the Beta wavelet family improves efficient results and performances presented in this paper for image compression context.

FAST LEARNING ALGORITHM OF WAVELET NETWORK BASED ON FAST WAVELET TRANSFORM

In this paper, a novel learning algorithm of wavelet networks based on the Fast Wavelet Transform (FWT) is proposed. It has many advantages compared to other algorithms, in which we solve the problem in previous works, when the weights of the hidden layer to the output layer are determined by applying the back propagation algorithm or by direct solution which requires to compute the matrix inversion, this may cause intensive computation when the learning data is too large. However, the new algorithm is realized by iterative application of FWT to compute the connection weights. Furthermore, we have extended the novel learning algorithm by using Levenberg–Marquardt method to optimize the learning functions. The experimental results have demonstrated that our model is remarkably more refreshing than some of the previously established models in terms of both speed and efficiency.

A NOVEL APPROACH FOR FACE RECOGNITION BASED ON FAST LEARNING ALGORITHM AND WAVELET NETWORK THEORY

This paper presents a new approach of face recognition based on wavelet network using 2D fast wavelet transform and multiresolution analysis. This approach is divided in two stages: the training stage and the recognition stage. The first consists to approximate every training face image by a wavelet network. The second consists in recognition of a new test image by comparing it to all the training faces, the distances between this test face and all images from the training set are calculated in order to identify the searched person. The usual training algorithms presents some disadvantages when the weights of the wavelet network are computed by applying the back-propagation algorithm or by direct solution which requires computing an inversion of matrix, this computation may be intensive when the learning data is too large. We present in this paper our solutions to overcome these limitations. We propose a novel learning algorithm based on the 2D Fast Wavelet Transform. Furthermore, we have increased the performances of our algorithm by introducing the Levenberg–Marquardt method to optimize the learning functions and using the Beta wavelet which has at both an analytical expression and wavelet filter bank. Extensive empirical experiments are performed to compare the proposed method with other approaches as PCA, LDA, EBGM and RBF neural network using the ORL and FERET benchmarks.

Classification improvement of local feature vectors over the KNN algorithm

The KNN classification algorithm is particularly suited to be used when classifying images described by local features. In this paper, we propose a novel image classification approach, based on local descriptors and the KNN algorithm. The proposed scheme is based on a hierarchical categorization tree that uses both supervised and unsupervised classification techniques. The unsupervised one is based on a hierarchical lattice vector quantization algorithm, while the supervised one is based on both feature vectors labelling and supervised feature selection method. The proposed tree improves the effectiveness of local feature vector classification and outperforms the exact KNN algorithm in terms of categorization accuracy.

PYRAMIDAL HYBRID APPROACH: WAVELET NETWORK WITH OLS ALGORITHM-BASED IMAGE CLASSIFICATION

Taking advantage of both the scaling property of wavelets and the high learning ability of neural networks, wavelet networks have recently emerged as a powerful tool in many applications in the field of signal processing such as data compression, function approximation as well as image recognition and classification. A novel wavelet network-based method for image classification is presented in this paper. The method combines the Orthogonal Least Squares algorithm (OLS) with the Pyramidal Beta Wavelet Network architecture (PBWN). First, the structure of the Pyramidal Beta Wavelet Network is proposed and the OLS method is used to design it by presetting the widths of the hidden units in PBWN. Then, to enhance the performance of the obtained PBWN, a novel learning algorithm based on orthogonal least squares and frames theory is proposed, in which we use OLS to select the hidden nodes. In the simulation part, the proposed method is employed to classify colour images. Comparisons with some typical wavelet networks are presented and discussed. Simulations also show that the PBWN-orthogonal least squares (PBWN-OLS) algorithm, which combines PBWN with the OLS algorithm, results in better performance for colour image classification.

FBWN: An architecture of fast beta wavelet networks for image classification

Image classification is an important task in computer vision. In this paper, we propose a supervised method for image classification based on a fast beta wavelet networks (FBWN) model. First, the structure of the wavelet network is detailed. Then, to enhance the performance of wavelet networks, a novel learning algorithm based on the Fast Wavelet Transform (FWTLA) is proposed. It has many advantages compared to other algorithms, in which we solve the problem of the previous works, when the weights of the hidden layer to the output layer are determinate by applying the back propagation algorithm or by direct solution which requires to compute matrix inversion, this may be intensive computation when the learning data is too large. However, the new algorithm is realized by the iterative application of FWT to compute connection weights. In the simulation part, the proposed method is employed to classify images. Comparisons with classical wavelet network classifier are presented and discussed. Results of comparison have shown that the FBWN model performs better than the previously established model in the context of training run time and classification rate.

Wavelet network for recognition system of Arabic word

Focusing on the development of new technologies of information, research in the speech communication field is an activity in full expansion. Several disciplines and skills interact in order to improve performance of Human Machine Communication Systems (HMC). In order to increase the performance of these systems, various techniques, including Hidden Markov Models (HMM) and Neural Network (NN), are implemented.In this paper, we advance a new approach for modelling of acoustic units and a new method for speech recognition, especially recognition of Arabic word, adapting to this new type of modelling based on Wavelet Network (WN). The new recognition system is a hybrid classifier. It is based on NN as a general model and the wavelets assume the role of activation function.Our approach of speech recognition is divided into two parts: training, and recognition phases. The training stage is based on audio corpus. After converting all training signals from original format to a specific parameterisation, each acoustic vector will be modelled by WN. These vectors will refine and cover all signal properties in one model. It consists in generating a WN for every training signal. The recognition phase is divided into three steps. The first is to extract features from the input vector to be recognized. The second is to estimate all resulting vectors from training WN. The third is to evaluate the distance between the vector to be recognized and the reconstructed vectors.The obtained results shows that our system, based on WN, is very competitive compared to systems based on HMM.

Video Watermarking Based on Neural Networks

In this paper, we propose a novel digital video watermarking scheme based on multi resolution motion estimation and artificial neural network. A multi resolution motion estimation algorithm is adopted to preferentially allocate the watermark to coefficients containing motion. In addition, embedding and extraction of the watermark are based on the relationship between a wavelet coefficient and its neighbors. A neural network is given to memorize the relationships between coefficients in a 3times3 block of the image. Experimental results show that embedding watermark where picture content is moving is less perceptible. Further, it shows that the proposed scheme is robust against common video processing attacks

Graph-based approach for human action recognition using spatio-temporal features

Due to the exponential growth of the video data stored and uploaded in the Internet websites especially YouTube, an effective analysis of video actions has become very necessary. In this paper, we tackle the challenging problem of human action recognition in realistic video sequences. The proposed system combines the efficiency of the Bag-of-visual-Words strategy and the power of graphs for structural representation of features. It is built upon the commonly used Space-Time Interest Points (STIP) local features followed by a graph-based video representation which models the spatio-temporal relations among these features. The experiments are realized on two challenging datasets: Hollywood2 and UCF YouTube Action. The experimental results show the effectiveness of the proposed method.

Neural solutions to interact with computers by hand gesture recognition

This paper attempts to present a vision-based interface which interacts with computers by hand gesture recognition. This work aims at creating a natural and intuitive application employing both static and dynamic hand gestures. The proposed application can be summarized in three main steps: hands detection in a video, hands tracking and converting hand shapes or trajectories into computer commands. To accomplish this application, a classification phase is paramount whether at the part of hand detection, or at the phase of “commanding computers”. For this reason, we have proposed to use a wavelet network classifier (WNC) learnt by fast wavelet transform (FWT). To emphasize the robustness of this classifier, we have used a neural network classifier (NNC) version in order to compare the two classifiers’ performances aiming at proving the strength of our proposed one. Global rates given by experimental results show the effectiveness of our proposed approaches of hand detection, hand tracking and hand gesture recognition. The comparison of the two classifier’s result helps to choose the best classifier, which can improve the performances of our application.