Comparative Analysis of Content-Based Image Retrieval Systems

Miroslav Marinov, I. Valova, Yordan Kalmukov
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引用次数: 10

Abstract

Content-based image retrieval methods in present days are used in modern social media and search engines. They give techniques for analyzing, organizing, processing and searching through million images uploaded daily in the internet. For that reason, searching is the most critical process in CBIR. It should be accurate and complete in reasonable amount of time. Proper metadata should be extracted from the images and used to meet the performance requirements. From the other hand, to meet the subsequent processing this metadata should be indexed and stored in appropriate way.This paper describes some of the most popular image extraction and analysis systems known as Content Based Image Retrieval Systems (CBIR). It reveals how examined different CBIR systems work and how closer are generated similarity results. At the end, information is summarized, and conclusions are made regarding existing solutions and methods used in these applications.
基于内容的图像检索系统的比较分析
目前基于内容的图像检索方法被用于现代社交媒体和搜索引擎。他们提供了分析、组织、处理和搜索每天在互联网上上传的数百万张图片的技术。因此,搜索是cir中最关键的过程。它应该在合理的时间内准确和完整。应该从映像中提取适当的元数据,并用于满足性能需求。另一方面,为了满足后续处理,应该以适当的方式对该元数据进行索引和存储。本文介绍了一些最流行的图像提取和分析系统,即基于内容的图像检索系统(CBIR)。它揭示了不同的CBIR系统是如何工作的,以及产生的相似结果有多接近。最后,对信息进行了总结,并对这些应用中使用的现有解决方案和方法进行了总结。
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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