Multimedia Tools and Applications

, Volume 77, Issue 7, pp 8495–8510 | Cite as

VQ codebook design using modified K-means algorithm with feature classification and grouping based initialization

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Abstract

Vector quantization (VQ) has been successfully used in data compression and feature extraction areas. Codebook design is the essential step of VQ. The K-means algorithm is a famous data clustering technique which is also an efficient codebook design scheme. The main disadvantages of K-means algorithm lie in that the initial cluster centroids greatly affect the convergence speed and the final clustering performance. In the past two decades, many codebook initialization techniques have been proposed. However, most of these techniques do not make full use of the features of the training vectors, and some techniques require high extra computational load. This paper presents an efficient and simple technique for the conventional K-means algorithm based on feature classification and grouping. Firstly, all training vectors are classified into sixteen categories based on a two-level classifier including an edge classifier and a contrast classifier. Then, the training vectors in each category are sorted based on their norm values and divided into groups. Each group has the same size, and the centroid vector of each group is calculated as an initial codeword. Experimental results show that, compared with several typical initialization techniques, our technique can obtain a better codebook along with a faster convergence speed in a shorter time.

Keywords

Vector quantization Codebook design Initial codebook generation K-means algorithm Image compression 

Notes

Acknowledgements

This work was supported partially by the financial support from the National Nature Science Foundation of China under grants No. 61633019 and No. 61272020 and Zhejiang Provincial Natural Science Foundation of China under grant No. LZ15F030004 and Ningbo Science &Technology Plan Project (2014B82015).

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Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  • Lang Wang
    • 1
  • Zhe-Ming Lu
    • 2
  • Long-Hua Ma
    • 1
  • Ya-Pei Feng
    • 2
  1. 1.School of Information Science and Engineering, Ningbo Institute of TechnologyZhejiang UniversityNingboPeople’s Republic of China
  2. 2.School of Aeronautics and AstronauticsZhejiang UniversityHangzhouPeople’s Republic of China

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