A Fuzzy Expert System for Concept-Based Image Indexing and Retrieval

I. A. Azzam, C. Leung, J. F. Horwood
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引用次数: 17

Abstract

Image indexing and retrieval using a concept-based approach involves extraction, modelling and indexing of image content information. Computer vision offers a variety of techniques for searching images in large collections. We propose a method that enables components of an image to be categorised on the basis of their relative importance in combination with filtered representations. Our method concentrates on matching subparts of images, defined in a variety of ways, in order to find particular objects. These ideas are illustrated with a variety of examples. We focus on Concept-based Image Indexing and Retrieval (CIIR), using a fuzzy expert systems, density measure, supporting factors and other attributes of image components to identify and retrieve images accurately and efficiently.
基于概念的图像索引与检索模糊专家系统
使用基于概念的方法进行图像索引和检索涉及图像内容信息的提取、建模和索引。计算机视觉为在大型集合中搜索图像提供了多种技术。我们提出了一种方法,该方法使图像的组件能够根据其相对重要性与过滤表示相结合进行分类。我们的方法专注于匹配以各种方式定义的图像子部分,以找到特定的对象。这些观点是用各种各样的例子来说明的。基于概念的图像索引与检索(CIIR),利用模糊专家系统、密度测度、支持因子和图像成分的其他属性来准确、高效地识别和检索图像。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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