Title :
An unsupervised fuzzy-neuro quantiser for image compression
Author :
Madiafi, Mohammed ; Bouroumi, Abdelaziz
Author_Institution :
Modeling & Instrum. Lab., Hassan II Mohammedia-Casablanca Univ. (UH2MC), Casablanca, Morocco
Abstract :
We propose a competitive fuzzy-neuro model for image compression. This model is based on a new unsupervised fuzzy learning algorithm, designed for optimal training of competitive neural networks. Experimental results show that the proposed model can perform better than other well-known methods of its category, including FCM and IFLVQ. Typical examples of these results are presented and discussed.
Keywords :
fuzzy neural nets; image coding; unsupervised learning; FCM; IFLVQ; competitive fuzzy-neuro model; competitive neural networks; image compression; optimal training; unsupervised fuzzy learning algorithm; unsupervised fuzzy-neuro quantiser; Boats; Image coding; Image reconstruction; Integrated circuits; Vectors; competitive neural networks; image compression; unsupervised learning; vector quantization;
Conference_Titel :
Multimedia Computing and Systems (ICMCS), 2012 International Conference on
Conference_Location :
Tangier
Print_ISBN :
978-1-4673-1518-0
DOI :
10.1109/ICMCS.2012.6320219
