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Content-based image retrieval using visually significant point features

机译:使用视觉上重要的点特征进行基于内容的图像检索

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摘要

This paper presents a new image retrieval scheme using visually significant point features. The clusters of points around significant curvature regions (high, medium, and weak type) are extracted using a fuzzy set theoretic approach. Some invariant color features are computed from these points to evaluate the similarity between images. A set of relevant and non-redundant features is selected using the mutual information based minimum redundancy-maximum relevance framework. The relative importance of each feature is evaluated using a fuzzy entropy based measure, which is computed from the sets of retrieved images marked relevant and irrelevant by the users. The performance of the system is evaluated using different sets of examples from a general purpose image database. The robustness of the system is also shown when the images undergo different transformations.
机译:本文提出了一种使用视觉上重要的点特征的新图像检索方案。使用模糊集理论方法提取显着曲率区域(高,中和弱类型)周围的点簇。从这些点计算出一些不变的颜色特征,以评估图像之间的相似性。使用基于互信息的最小冗余-最大相关性框架选择一组相关和非冗余特征。使用基于模糊熵的度量来评估每个特征的相对重要性,该度量是根据用户标记为相关和不相关的检索图像集计算得出的。使用通用图像数据库中的不同示例集评估系统的性能。当图像经历不同的变换时,还显示了系统的鲁棒性。

著录项

  • 来源
    《Fuzzy sets and systems》 |2009年第23期|3323-3341|共19页
  • 作者单位

    Center for Soft Computing Research, Indian Statistical Institute, 203, B. T. Road, Kolkata 700 108, India;

    Center for Soft Computing Research, Indian Statistical Institute, 203, B. T. Road, Kolkata 700 108, India Machine Intelligence Unit, Indian Statistical Institute, 203, B. T. Road, Kolkata 700 108, India;

    Center for Soft Computing Research, Indian Statistical Institute, 203, B. T. Road, Kolkata 700 108, India Machine Intelligence Unit, Indian Statistical Institute, 203, B. T. Road, Kolkata 700 108, India;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    content-based image retrieval; high curvature points; fuzzy feature evaluation index; color; invariant moments;

    机译:基于内容的图像检索;高曲率点模糊特征评价指标颜色;不变时刻;

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