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A Content Based Image Retrieval Method Based on K-Means Clustering Technique

机译:基于K均值聚类技术的基于内容的图像检索方法

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摘要

With the appearance of many devices that are used in image acquisition comes a large number of images every day. The rapid access to these huge collections of images and retrieval of similar images (Query) from this huge collection of images presents major challenges and requires efficient algorithms. The main goal of the proposed system is to provide an accurate result with lower computational time. For this purpose, the authors apply a new method based on k-means clustering technique to match image's descriptors. This article provides a detailed view of the solution the authors have adopted and which perfectly meets their needs. For validation, they apply all of these techniques on two image databases in order to evaluate the performance of their system.
机译:随着用于图像采集的许多设备的出现,每天都会出现大量图像。快速访问这些巨大的图像集合以及从这个巨大的图像集合中检索相似的图像(查询)提出了重大挑战,并需要高效的算法。提出的系统的主要目标是以更少的计算时间提供准确的结果。为此,作者应用了一种基于k均值聚类技术的新方法来匹配图像的描述符。本文提供了作者采用的并且完全满足他们需求的解决方案的详细视图。为了进行验证,他们将所有这些技术应用于两个图像数据库,以评估其系统的性能。

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  • 作者单位

    Moulay Ismail University, Faculty of Sciences and technology, Department of Computer Science, ASIA Team M2I Laboratory, Meknes, Morocco;

    Moulay Ismail University, Faculty of Sciences and technology, Department of Computer Science, ASIA Team M2I Laboratory, Meknes, Morocco;

    Moulay Ismail University, Faculty of Sciences and technology, Department of Computer Science, ASIA Team M2I Laboratory, Meknes, Morocco;

    Moulay Ismail University, Faculty of Sciences and technology, Department of Computer Science, ASIA Team M2I Laboratory, Meknes, Morocco;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    CBIR; Classification; K-Means; Segmentation; Similarity Measure;

    机译:CBIR;分类;K-均值分割;相似度;
  • 入库时间 2022-08-17 13:37:58

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