Patané G, Russo M
Institute of Computer Science and Telecommunications, Faculty of Engineering, University of Catania, Italy.
Neural Netw. 2001 Nov;14(9):1219-37. doi: 10.1016/s0893-6080(01)00104-6.
Clustering applications cover several fields such as audio and video data compression, pattern recognition, computer vision, medical image recognition, etc. In this paper, we present a new clustering algorithm called Enhanced LBG (ELBG). It belongs to the hard and K-means vector quantization groups and derives directly from the simpler LBG. The basic idea we have developed is the concept of utility of a codeword, a powerful instrument to overcome one of the main drawbacks of clustering algorithms: generally, the results achieved are not good in the case of a bad choice of the initial codebook. We will present our experimental results showing the ELBG is able to find better codebooks than previous clustering techniques and the computational complexity is virtually the same as the simpler LBG.
聚类应用涵盖多个领域,如音频和视频数据压缩、模式识别、计算机视觉、医学图像识别等。在本文中,我们提出了一种名为增强LBG(ELBG)的新聚类算法。它属于硬K均值向量量化组,直接源自更简单的LBG算法。我们提出的基本思想是码字效用的概念,这是一种强大的工具,可用于克服聚类算法的一个主要缺点:通常,当初始码本选择不佳时,所获得的结果并不理想。我们将展示实验结果,表明ELBG能够找到比以前的聚类技术更好的码本,并且计算复杂度与更简单的LBG算法基本相同。