Paper Info
Title
A Fast Search Algorithm for Vector Quantization Based on Associative Memories
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.
Year
DOI
Venue
2008
10.1007/978-3-540-85920-8_60
CIARP
Keywords
Field
DocType
encoding phase,eam training stage,vector quantization,new fast search algorithm,lbg algorithm,fsa-eamvq process,associative network,extended associative memories,fast search algorithm,proposed algorithm,input vector,search algorithm,associative memory,computational complexity
Training set,Associative property,Search algorithm,Linde–Buzo–Gray algorithm,Pattern recognition,Computer science,Vector quantization,Artificial intelligence,Codebook,Encoding (memory),Computational complexity theory
Conference
Volume
ISSN
Citations 
5197
0302-9743
2
PageRank 
References 
Authors
0.38
8
4
Name
Order
Citations
PageRank
Enrique Guzmán1183.19
Oleksiy Pogrebnyak22810.20
Luis Sánchez Fernández317319.82
Cornelio Yáñez-Márquez415326.34