Abstract
With the rapid development of Internet technology, the number of images has shown explosive growth. Content-based image retrieval is an important research topic in the field of computer vision, and it aims to efficiently retrieve target images in massive image databases. Due to the different image characteristics in various fields, the previous image retrieval based on single content feature (color, shape, texture, etc.) can no longer meet the application requirements of image retrieval in related fields. In order to efficiently retrieve specific target images in related fields, based on the typical image content feature namely the color moments, image hash feature including perceptual hash, average hash, and difference hash was respectively fused with the color moments to retrieve images in this paper. Aiming to improve the retrieval efficiency, a weighted lightweight image retrieval method based on linear regression was proposed. Linear regression analysis was performed on image perceptual hash, average hash and color moments. The similarity obtained by the faster hash feature was used to replace the color moments. Finally, the hash feature was merged with the new color moments to retrieve the image. Experimental results show that compared with the direct fusion of image hash feature and color moments, the weighted lightweight image retrieval method based on linear regression proposed in this paper can improve the retrieval efficiency while maintaining the retrieval accuracy.
Supported by Natural Science Basic Research Program of Shaanxi(No.2017JQ6026) and Yulin Science and Technology Plan Production-University-Research Program(No.2014CXY-08-01).
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References
Rui, Y., Huang, T.S., Chang, S.-F.: Image retrieval: current techniques, promising directions, and open issues. J. Vis. Commun. Image Represent. 10, 39–62 (1999)
Piras, L., Giacinto, G.: Information fusion in content based image retrieval: a comprehensive overview. Inform. Fusion 37, 50–60 (2017)
Raghuwanshi, G., Tyagi, V.: A novel technique for content based image retrieval based on region-weight assignment. Multimed. Tools Appl. 78(2), 1889–1911 (2018). https://doi.org/10.1007/s11042-018-6333-6
Cui, S., Xiong, S., Liu, C., Chen, M.: A survey of content-based medical image retrieval methods. J. Chongqing Univ. Technol. (Nat. Sci.) 12, 113–121 (2018)
Zhou, W., Li, H., Tian, Q.: Recent advance in content-based image retrieval: a literature survey (2017). arXiv preprint arXiv:1706.06064
Peng, J., Su, Y., Xue, X.: SAR image feature retrieval method based on deep learning and synchronic matrix. Comput. Sci. 46(S1), 196–199+204 (2019)
Zhang, C., Yang, X., Qiquan, X., Chen, S.: Hash fast retrieval and image matching based on sift feature. Modern Electron. Techn. 42(12), 127–131 (2019)
Shuang, Z.J.C., Lili, H.: Clothing image retrieval method based on deep learning. Comput. Syst. Appl. 28(03), 229–234 (2019)
He, X., Tang, Y., Wang, L., Chen, P., Yuan, G.: Estimating graphlets via two common substructures aware sampling in social networksmultitask hierarchical image retrieval technology based on faster rcnnh. Comput. Sci. 46(03), 303–313 (2019)
Pavithra, L.K., Sharmila, T.S.: An efficient framework for image retrieval using color, texture and edge features. Comput. Elect. Eng. 70, 580–593 (2018)
Qiao, H., Deng, Z., Xue, J., Song, Q.: Research of image retrieval method based on improved feature. J. Northwest. Polytech. Univ. 36(4), 742–747 (2018)
Liu, Z.: Research on key techniques of image perceptual hashing. J. Harbin Inst. Technol. (2013)
Niu, X., Jiao, Y.: An overview of perceptual hashing. Acta Electron. Sinica 7, 1405–1411 (2008)
Xiaoqiang, L., Zheng, X., Li, X.: Latent semantic minimal hashing for image retrieval. IEEE Trans. Image Process. 26(1), 355–368 (2016)
Zhu, L., Shen, J., Xie, L., Cheng, Z.: Unsupervised visual hashing with semantic assistant for content-based image retrieval. IEEE Trans. Knowl. Data Eng. 29(2), 472–486 (2016)
Alzu’bi, A., Amira, A., Ramzan, N.: Content-based image retrieval with compact deep convolutional features. Neurocomputing 249, 95–105 (2017)
Ahmed, K.T., Ummesafi, S., Iqbal, A.: Content based image retrieval using image features information fusion. Information Fusion 51, 76–99 (2019)
Varish, N., Kumar, S., Pal, A.K.: A novel similarity measure for content based image retrieval in discrete cosine transform domain. Fundam. Inform. 156(2), 209–235 (2017)
Zhang, W., Kong, X., You, X.: Secure and robust image perceptual hashing. J. Southeast Univ. (Nat. Sci. Ed.), (S1), 188–192 (2007)
Fei, M., Ju, Z., Zhen, X., Li, J.: Real-time visual tracking based on improved perceptual hashing. Multimedia Tools Appl. 76(3), 4617–4634 (2016). https://doi.org/10.1007/s11042-016-3723-5
Wang, H., Yin, W., Wang, L., Jianghao, H., Qiao, W.: Fast edge extraction algorithm based on HSV color space. J. Shanghai Jiaotong Univ. 53(07), 765–772 (2019)
Krizhevsky, A., Nair, V., Hinton, G.: Cifar-10 and cifar-100 datasets, 6:1 (2009). https://www.cs.toronto.edu/kriz/cifar.html
Griffin, G., Holub, A., Perona, P.: Caltech-256 object category dataset (2007)
Chathurika, K.B.A.B., Jayasinghe, P.K.S.C.: A revised averaging algorithm for an effective feature extraction in component-based image retrieval system. In: 2015 IEEE International Advance Computing Conference (IACC), pp. 1153–1157 (2015)
Kobayashi, K., Chen, Q.: Image retrieval using features in spatial and frequency domains based on block-division. In: 2015 International Conference on Computational Science and Computational Intelligence (CSCI), pp. 448–453 (2015)
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Zhang, L., Zheng, X., Dang, X., Zhang, J. (2020). Weighted Lightweight Image Retrieval Method Based on Linear Regression. In: Ben Hedia, B., Chen, YF., Liu, G., Yu, Z. (eds) Verification and Evaluation of Computer and Communication Systems. VECoS 2020. Lecture Notes in Computer Science(), vol 12519. Springer, Cham. https://doi.org/10.1007/978-3-030-65955-4_20
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