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Full-Text Articles in Physical Sciences and Mathematics
Multi-Resolution Mean-Shift Algorithm For Vector Quantization, P L. M Bouttefroy, A Bouzerdoum, A Beghdadi, S L. Phung
Multi-Resolution Mean-Shift Algorithm For Vector Quantization, P L. M Bouttefroy, A Bouzerdoum, A Beghdadi, S L. Phung
Faculty of Informatics - Papers (Archive)
The generation of stratified codebooks, providing a subset of vectors at different scale levels, has become necessary with the emergence of embedded coder/decoder for scalable image and video formats. We propose an approach based on mean-shift, invoking the multi-resolution framework to generate codebook vectors. Applied to the entire image, mean-shift is slow because it requires each sample to converge to a mode of the distribution. The procedure can be sped up with three simple assumptions: kernel truncation, code attraction and trajectory attraction. Here we propose to apply the mean-shift algorithm to the four image subbands generated by a DWT, namely …
A Training Algorithm For Sparse Ls-Svm Using Compressive Sampling, Jie Yang, Son Lam Phung, Abdesselam Bouzerdoum
A Training Algorithm For Sparse Ls-Svm Using Compressive Sampling, Jie Yang, Son Lam Phung, Abdesselam Bouzerdoum
Faculty of Informatics - Papers (Archive)
Least Squares Support Vector Machine (LS-SVM) has become a fundamental tool in pattern recognition and machine learning. However, the main disadvantage is lack of sparseness of solutions. In this article Compressive Sampling (CS), which addresses the sparse signal representation, is employed to find the support vectors of LS-SVM. The main difference between our work and the existing techniques is that the proposed method can locate the sparse topology while training. In contrast, most of the traditional methods need to train the model before finding the sparse support vectors. An experimental comparison with the standard LS-SVM and existing algorithms is given …
A Particle Swarm Optimization Algorithm Based On Orthogonal Design, Jie Yang, Abdesselam Bouzerdoum, Son Lam Phung
A Particle Swarm Optimization Algorithm Based On Orthogonal Design, Jie Yang, Abdesselam Bouzerdoum, Son Lam Phung
Faculty of Informatics - Papers (Archive)
The last decade has witnessed a great interest in using evolutionary algorithms, such as genetic algorithms, evolutionary strategies and particle swarm optimization (PSO), for multivariate optimization. This paper presents a hybrid algorithm for searching a complex domain space, by combining the PSO and orthogonal design. In the standard PSO, each particle focuses only on the error propagated back from the best particle, without “communicating” with other particles. In our approach, this limitation of the standard PSO is overcome by using a novel crossover operator based on orthogonal design. Furthermore, instead of the “generating-and-updating” model in the standard PSO, the elitism …