TY - GEN
T1 - A fast search algorithm for vector quantization based on associative memories
AU - Guzmán, Enrique
AU - Pogrebnyak, Oleksiy
AU - Fernández, Luis Sánchez
AU - Yáñez-Márquez, Cornelio
N1 - Funding Information:
Received August 21, 2006; revised November 10, 2006 *Corresponding author. Tel: +86-451-84800297; E-mail: jichenfeng@hrbcu.edu.cn This work was supported by the National Natural Science Foundation of China (No. 30672595).
PY - 2008
Y1 - 2008
N2 - 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-EAM VQ 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.
AB - 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-EAM VQ 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.
KW - Associative memories
KW - Fast search
KW - Image coding
KW - Vector quantization
UR - http://www.scopus.com/inward/record.url?scp=55349142892&partnerID=8YFLogxK
U2 - 10.1007/978-3-540-85920-8_60
DO - 10.1007/978-3-540-85920-8_60
M3 - Contribución a la conferencia
SN - 3540859195
SN - 9783540859192
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 487
EP - 495
BT - Progress in Pattern Recognition, Image Analysis and Applications - 13th Iberoamerican Congress on Pattern Recognition, CIARP 2008, Proceedings
T2 - 13th Iberoamerican Congress on Pattern Recognition, CIARP 2008
Y2 - 9 September 2008 through 12 September 2008
ER -