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A novel color image retrieval method based on texture and deep features

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Abstract

Enlightened by the currently prevalent great complementarity of traditional features and deep features in image content expression, we propose a novel color image retrieval method based on texture and deep features which represent the image with the combination of HOG histogram, Hu invariable moments and deep features. Firstly, HOG features based on local binary pattern are extracted from the image using the proposed second-order full-directional derivative, which can express more gradient information through simplification the expression method of full-directional derivative. Meanwhile, considering that the combination of information entropy and color can better represent the image content, we propose a novel quaternion expression method for color image and calculate its Hu moment features, which represent color image in a simple way by combination of color and texture information. Secondly, we extract deep features from an improved VGG network structure. Finally, the hybrid features combining HOG histogram, the new Hu moments and deep information are used to represent a color image and to perform retrieve task. In order to prove the effectiveness of our method, three common databases (INRIA Holidays, Oxford 5 K and UKB) are used to prove the proposed algorithm. Experimental results show that the proposed scheme has better performance on the basis of lower feature dimension.

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Funding

This work was supported by National Natural Science Foundation of China (Grant 61,861,040), Natural Science Foundation of Gansu Province (20JR5RA518).

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Authors and Affiliations

  1. College of Computer Science and Engineering, Northwest Normal University, Lanzhou, 730070, Gansu, China

    Weiyi Wei, Wanru Wang, Yijing Yang & Yu Wang

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  1. Weiyi Wei
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  2. Wanru Wang
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  4. Yu Wang
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Correspondence to Wanru Wang.

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Wei, W., Wang, W., Yang, Y. et al. A novel color image retrieval method based on texture and deep features. Multimed Tools Appl 81, 659–679 (2022). https://doi.org/10.1007/s11042-021-11198-z

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  • DOI: https://doi.org/10.1007/s11042-021-11198-z

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