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首页> 外文会议>International Conference on Signal Processing(ICSP'06); 20061116-20; Guilin(CN) >AN EFFECTIVE METHOD OF IMAHE RETRIEVAL BASED ON MODIFIED FUZZY C-MEANS CLUSTERING SCHEME
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AN EFFECTIVE METHOD OF IMAHE RETRIEVAL BASED ON MODIFIED FUZZY C-MEANS CLUSTERING SCHEME

机译：基于改进的模糊C均值聚类方案的IMAHE检索有效方法

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摘要

With the development of Multimedia Network Technology and the rapid increase of image application, Content-based Image Retrieval (CBIR) becomes the most active one in multimedia information retrieval field. One of the key issues in CBIR is how to construct effective organization and index to enhance image retrieval speed. Clustering is a kind of effective method. This paper presents a modified fuzzy C-means (MFCM) clustering index scheme method. In addition, in order to reduce the time of clustering, high-dimension feature space is transformed into lower-dimension space by using Karhunen-Loeve (K-L) transformation. The clustering step is performed in lower-dimension space, and image retrieval is only performed in clustered prototypes. Experimental results show that MFCM applied to image retrieval is effectively, exact and real-time. The time of retrieval doesn't increase linearly with the extended image database. It is superior to traditional C-means and fuzzy C-means clustering algorithms.
机译：随着多媒体网络技术的发展和图像应用的迅速增长，基于内容的图像检索（CBIR）成为多媒体信息检索领域中最活跃的一种。 CBIR的关键问题之一是如何构建有效的组织和索引以提高图像检索速度。聚类是一种有效的方法。本文提出了一种改进的模糊C均值（MFCM）聚类索引方案方法。另外，为了减少聚类的时间，通过使用Karhunen-Loeve（K-L）变换将高维特征空间转换为低维空间。聚类步骤在较低维度的空间中执行，而图像检索仅在聚类的原型中执行。实验结果表明，MFCM用于图像检索是有效，准确和实时的。检索时间不会随扩展图像数据库线性增加。它优于传统的C均值和模糊C均值聚类算法。

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