Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/69456
標題: HYBRID GLOBAL/LOCAL SEARCH STRATEGIES FOR VQ CODEBOOK GENERATION BASED ON OTSU AND LBG ALGORITHM
作者: Huang, C.C.
Tsai, D.S.
Horng, G.
關鍵字: Vector quantization;Codebook generation;Histogram threshold;Clustering;LBG algorithm;Global optimal;Local optimal;vector quantization;steganography;histograms;transform;images
Project: International Journal of Innovative Computing Information and Control
期刊/報告no:: International Journal of Innovative Computing Information and Control, Volume 6, Issue 6, Page(s) 2645-2656.
摘要: 
Vector quantization algorithms have been extensively used for image compression, pattern recognition, image steganography, image retrieval, and anomaly intrusion detection. For large N(p) training vectors and N(c) clusters, vector quantization algorithms can hardly find the global optimal Classification without requiring a great deal of the squared Euclidean distance calculation. This paper proposes an efficient global division algorithm based on histogram threshold to improve computation time of the squared Euclidean distance from O(kN(p) log N(c)) to O(kN(p)N(c)). The experimental results and comparisons show that the global division algorithm can reduce computational complexity, find better codewords to improve the quality of the codebook and cooperate with the local search algorithm to tune it efficiently.
URI: http://hdl.handle.net/11455/69456
ISSN: 1349-4198
Appears in Collections:期刊論文

Show full item record
 
TAIR Related Article

Google ScholarTM

Check


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.