图像数据库聚类和基于内容的检索中的亲和关系发现

M. Shyu, Shu‐Ching Chen, Min Chen, Chengcui Zhang
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引用次数: 18

摘要

在本文中,我们提出了一个统一的框架,称为马尔可夫模型中介(MMM),以方便图像数据库聚类和提高查询性能。MMM框架的结构包括两个层次:局部mm和集成mm,它们分别通过有效的数据挖掘过程对单个图像数据库和一组图像数据库中的图像之间的亲和关系进行建模。通过一组包含不同维度和概念类别的不同数量图像的图像数据库,验证了MMM框架在数据库聚类和图像检索方面的有效性和效率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Affinity relation discovery in image database clustering and content-based retrieval
In this paper, we propose a unified framework, called Markov Model Mediator (MMM), to facilitate image database clustering and to improve the query performance. The structure of the MMM framework consists of two hierarchical levels: local MMMs and integrated MMMs, which model the affinity relations among the images within a single image database and within a set of image databases, respectively, via an effective data mining process. The effectiveness and efficiency of the MMM framework for database clustering and image retrieval are demonstrated over a set of image databases which contain various numbers of images with different dimensions and concept categories.
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