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CoMo: a scale and rotation invariant compact composite moment-based descriptor for image retrieval

机译:CoMo:用于图像检索的比例和旋转不变紧凑型基于矩的描述符

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摘要

Low level features play a significant role in image retrieval. Image moments can effectively represent global information of image content while being invariant under translation, rotation, and scaling. This paper presents CoMo: a moment based composite and compact low-level descriptor that can be used effectively for image retrieval and robot vision tasks. The proposed descriptor is evaluated by employing the Bag-of-Visual-Words representation over various well-known benchmarking image databases. The findings from the experimental evaluation provide strong evidence of high and competitive retrieval performance against various state-of-the-art local descriptors.
机译:低级特征在图像检索中起重要作用。图像矩可以有效地表示图像内容的全局信息,同时在平移,旋转和缩放下保持不变。本文介绍了CoMo:一种基于矩的复合且紧凑的低级描述符,可有效地用于图像检索和机器人视觉任务。通过在各种众所周知的基准图像数据库上采用“视觉袋”表示法来评估提出的描述符。实验评估的结果提供了有力的证据证明,与各种最新的本地描述符相比,它具有较高的竞争性和检索性能。

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