A Fast Search Algorithm for Vector Quantization Based on Associative Memories

Authors:
Enrique Guzmán;Oleksiy Pogrebnyak;Luis Sánchez Fernández;Cornelio Yáñez-Márquez
Affiliations:
Universidad Tecnológica de la Mixteca,;Centro de Investigación en Computación del Instituto Politécnico Nacional,;Centro de Investigación en Computación del Instituto Politécnico Nacional,;Centro de Investigación en Computación del Instituto Politécnico Nacional,
Venue:
CIARP '08 Proceedings of the 13th Iberoamerican congress on Pattern Recognition: Progress in Pattern Recognition, Image Analysis and Applications
Year:
2008

Citing 5
Cited 1

Vector quantization and signal compression

Vector quantization and signal compression
Self-Organizing Maps

Self-Organizing Maps
Image compression using self-organizing maps

Systems Analysis Modelling Simulation - Special issue: Digital signal processing and control
Real-Valued Pattern Classification Based on Extended Associative Memory

ENC '04 Proceedings of the Fifth Mexican International Conference in Computer Science
Design of an evolutionary codebook based on morphological associative memories

MICAI'07 Proceedings of the artificial intelligence 6th Mexican international conference on Advances in artificial intelligence

Vector Quantization Algorithm Based on Associative Memories

MICAI '09 Proceedings of the 8th Mexican International Conference on Artificial Intelligence

Quantified Score

Hi-index	0.00

Visualization

Abstract

One of the most serious problems in vector quantization is the high computational complexity at the encoding phase. This paper presents a new fast search algorithm for vector quantization based on Extended Associative Memories (FSA-EAM). In order to obtain the FSA-EAM, first, we used the Extended Associative Memories (EAM) to create an EAM-codebook applying the EAM training stage to the codebook produced by the LBG algorithm. The result of this stage is an associative network whose goal is to establish a relation between training set and the codebook generated by the LBG algorithm. This associative network is EAM-codebook which is used by the FSA-EAM. The FSA-EAMVQ process is performed using the recalling stage of EAM. This process generates a set of the class indices to which each input vector belongs. With respect to the LBG algorithm, the main advantage offered by the proposed algorithm is high processing speed and low demand of resources (system memory), while the encoding quality remains competitive.