与低水平和高水平特征相关的CBIR综述

Tamil Kodi, G. Rosline Nesa Kumari, S. Maruthu Perumal
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引用次数: 4

摘要

从海量图像信息中检索图像的方法被称为基于内容的多图像检索CBIR。CBIR是指感兴趣的标准分析空间。CBIR通过满足用户在图片类别内的查询,为用户与海量信息的交互铺平了道路。本文讨论了一种CBIR系统,该系统本身受到记录中图像符号所采用的选项的抑制,并共同研究了在提供查询图像的情况下处理图像支持的低级别和高级别选项的提取方法。这项工作的最大贡献可能是对低水平和高水平特征方法进行了全面的比较。为了以良好的方式检索图片,本文为受害提供了关联平台,可以专门针对每个低级别和高级别选项,并创建了关于高级别选项的澄清,将检索图像提供了许多与查询图像相关的方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Review of CBIR Related with Low Level and High Level Features
The method of retrieving pictures from the massive image info is termed as content based mostly image retrieval CBIR. CBIR is that the standard analysis space of interest. CBIR paves the approach of user interaction with giant info by satisfying their queries within the sort of pictures. This paper discusses the recital of a CBIR system that is in and of itself repressed by the options adopted to symbolize the pictures within the record and conjointly study the approaches of a spread of ways that deals with the extraction of options supported low and high level options of images with the query image provided. The most contribution of this work could be a comprehensive comparison between the low level and high level feature approaches to CBIR.To retrieve the pictures in a good manner this paper provides associate platform for victimization the ways which can able to specialize in each low level and high level options and created clarification regarding high level options will retrieve images a lot of relevant to the query image provided.
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