Paper Info
Title
Color quantization by pairwise clustering using a reduced graph
Abstract
Abstract The aim of this work is to investigate an improvement in the performance of the pairwise clustering algorithm (PCA) for color quantization of images, that algorithm uses a local error optimization strategy to generate near optimal quantization levels. We investigate the behavior of the accumulated error in the final images when, instead of computing distances between all pairs of colors, a reduced graph is used. We take advantage of a sorted vector of colors to reduce the number of neighbors considered by each node of the graph of distances.
Year
DOI
Venue
2001
10.1016/S1571-0653(04)00244-6
Electronic Notes in Discrete Mathematics
Keywords
Field
DocType
Color Quantization,Hierarchical Clustering,Graphs
Hierarchical clustering,Pairwise comparison,Graph,Combinatorics,Pattern recognition,Linde–Buzo–Gray algorithm,Vector quantization,Artificial intelligence,Cluster analysis,Quantization (signal processing),Color quantization,Mathematics
Journal
Volume
ISSN
Citations 
7
1571-0653
2
PageRank 
References 
Authors
0.38
2
2
Name
Order
Citations
PageRank
Asla Medeiros Sá1648.43
Paulo Cezar Pinto Carvalho27412.29