Opto-Electronic Engineering, Volume. 48, Issue 11, 210310(2021)

Multi-scale image retrieval based on HSV and directional gradient features

Jiang Man1, Zhang Haoxiang1, Cheng Deqiang1,2、*, Guo Lin1, Kou Qiqi3, and Zhao Lei1
Author Affiliations
  • 1[in Chinese]
  • 2[in Chinese]
  • 3[in Chinese]
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    Aiming at the problems of poor robustness of rotation change, high feature dimension, and long retrieval time of existing color image retrieval algorithms, this paper proposed an innovative image retrieval method by fusing color features and improved directional gradient features. It proposed an improved directional gradient algorithm based on the principal curvatures (P-FHOG) by combining the geometric curvature information of the image surface into the FHOG descriptor from multiple scales. At the same time, the color information of the image was further fused to obtain the multi-scale image retrieval method based on the color features and the improved directional gradient features (CP-FHOG). The experiment was compared with the advanced image retrieval methods on the Corel-1000 and Coil-100 data sets, and the average accuracy rates of 85.89% and 93.38% were achieved, respectively. The results show that the proposed algorithm is more accurate and robust (in rotation change) than other algorithms.

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    Jiang Man, Zhang Haoxiang, Cheng Deqiang, Guo Lin, Kou Qiqi, Zhao Lei. Multi-scale image retrieval based on HSV and directional gradient features[J]. Opto-Electronic Engineering, 2021, 48(11): 210310

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    Paper Information

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    Received: Sep. 24, 2021

    Accepted: --

    Published Online: Feb. 25, 2022

    The Author Email: Deqiang Cheng (cdqcumt@126.com)

    DOI:10.12086/oee.2021.210310

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