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Food image classification and image retrieval based on visual features and machine learning

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Abstract

Research on image retrieval and classification in the food field has become one of the more and more concerned research topics in the field of multimedia analysis and applications. In recent years, with the rapid development of the Internet industry and multimedia technology, image classification and retrieval technology has become a research hotspot at home and abroad. Traditional keyword-based image retrieval and image classification have been unable to meet people’s daily needs; so, image recognition methods based on image content came into being. The most representative of image feature description methods are mainly two aspects: image visual features and image abstract semantics extracted based on machine learning algorithms. These two algorithms have their own key points in describing images, which are difficult to achieve the desired results in image classification and image retrieval. Based on this, this paper proposes research on food image classification and image retrieval methods based on visual features and machine learning. This paper proposes a food image retrieval and classification method based on Faster R-CNN network. This paper selects food image sets from the visual gene database to fine-tune the Faster R-CNN network to ensure the accuracy of Faster R-CNN food area detection, and experimented on the Dish-233 food dataset, which is a subset of the dish dataset, including 233 dishes and 49,168 images. The experimental results in this paper show that the performance of this method is better than other methods in terms of image classification performance. Compared with CNN-GF, the performance is improved by 5%. In terms of image retrieval, this method also shows its superiority This proves that compared with other methods, the proposed method has more discriminative visual features, and its performance has been improved in food image retrieval and classification tasks.

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References

  1. Nagata, T.: Segmentation, Classification, and High Resolution of Image by Machine Learning. Nihon Igaku Butsuri Gakkai kikanshi Japn. J. Med. Phys. 36(1), 23–28 (2016)

    Google Scholar 

  2. Kim, H., Kim, J., Kim, Y.: Design of network threat detection and classification based on machine learning on cloud computing. Clust. Comput. 22(Suppl 1), 1–10 (2019)

    Google Scholar 

  3. Lü, F., Han, M.: Hyperspectral remote sensing image classification based on deep extreme learning machine. J. Dalian Univ. Technol. 58(2), 166–173 (2018)

    Google Scholar 

  4. Baltrusaitis, T., Ahuja, C., Morency, L.-P.: Multimodal machine learning: a survey and taxonomy. IEEE Trans. Pattern Anal. Mach. Intell. 41(2), 423–443 (2019)

    Article  Google Scholar 

  5. Singh, V.P., Srivastava, R., Pathak, Y.: Content-based image retrieval based on supervised learning and statistical-based moments. Mod. Phys. Lett. B 33(3), 1950213 (2019)

    Article  MathSciNet  Google Scholar 

  6. He, X., Zhang, C., Zhang, L.: A-optimal projection for image representation. IEEE Trans. Pattern Anal. Mach. Intell. 38(5), 1009–1015 (2016)

    Article  Google Scholar 

  7. Shen, F., Yang, Y., Liu, L.: Asymmetric binary coding for image search. IEEE Trans. Multimed. 19(9), 2022–2032 (2017)

    Article  Google Scholar 

  8. Keming, M., Zhuofu, D.: Lung nodule image classification based on ensemble machine learning. J. Med. Imaging Health Inf. 6(7), 1679–1685 (2016)

    Article  MATH  Google Scholar 

  9. Mishra, M., Rout, P.K.: Detection and classification of micro-grid faults based on HHT and machine learning techniques. Iet Gener. Transm. Distrib 12(2), 388–397 (2017)

    Article  Google Scholar 

  10. Alkasassbeh, M.: An empirical evaluation for the intrusion detection features based on machine learning and feature selection methods. J. Theor. Appl. Inf. Technol. 95(22), 5962–5976 (2017)

    Google Scholar 

  11. Liu, F., Jiao, L., Hou, B.: POL-sar image classification based on Wishart DBN and local spatial information. IEEE Trans. Geosci. Remote Sens. 54(6), 1–17 (2016)

    Article  Google Scholar 

  12. Li, P., Wang, Q., Zeng, H.: Local log-euclidean multivariate gaussian descriptor and its application to image classification. IEEE Trans. Pattern Anal. Mach. Intell. 39(4), 1–1 (2016)

    Google Scholar 

  13. Zhu, W., Zhang, H., Ni, W.: Image classification based on ICA-WP feature of EEG signal. Technol. Health Care Off. J. Eur. Soc. Eng. Med. 24(s2), S551 (2016)

    Google Scholar 

  14. Li, Y., Zhang, H., Xue, X.: Deep learning for remote sensing image classification: a survey. Wiley Interdiscip. Rev. Data Min. Knowl. Discov. 12, e1264 (2018)

    Google Scholar 

  15. Fang, X.: Member, IEEE. Robust latent subspace learning for image classification. IEEE Trans. Neural Netw. Learn. Syst. 29(6), 2502–2515 (2018)

    Article  MathSciNet  Google Scholar 

  16. Gao, S., Duan, L., Tsang, I.W.: DEFEATnet—a deep conventional image representation for image classification. IEEE Trans. Circ. Syst. Video Technol. 26(3), 494–505 (2016)

    Article  Google Scholar 

  17. Lin, D., Fu, K., Wang, Y.: MARTA GANs: unsupervised representation learning for remote sensing image classification. IEEE Geosci. Remote Sens. Lett. 14(11), 2092–2096 (2016)

    Article  Google Scholar 

  18. Lu, X., Zheng, X., Li, X.: Latent semantic minimal hashing for image retrieval. IEEE Trans. Image Process. 26(1), 355–368 (2017)

    Article  MathSciNet  MATH  Google Scholar 

  19. Zhou, W., Newsam, S., Li, C.: Learning low dimensional convolutional neural networks for high-resolution remote sensing image retrieval. Remote Sens. 9(5), 489 (2016)

    Article  Google Scholar 

  20. Angeli, A., Ferri, M., Tomba, I.: Symmetric functions for fast image retrieval with persistent homology. Math. Methods Appl. Sci. 41(18), 9567–9577 (2018)

    Article  MathSciNet  MATH  Google Scholar 

  21. Dhara, A.K., Mukhopadhyay, S., Dutta, A.: Content-based image retrieval system for pulmonary nodules: assisting radiologists in self-learning and diagnosis of lung cancer. J. Dig. Imaging 30(1), 63–77 (2016)

    Article  Google Scholar 

  22. Zhang, J., Peng, Y.: SSDH: semi-supervised deep hashing for large scale image retrieval. IEEE Trans. Circ. Syst. Video Technol. 29(99), 1–1 (2016)

    Google Scholar 

  23. Zhu, L., Shen, J., Xie, L.: Unsupervised visual hashing with semantic assistant for content-based image retrieval. IEEE Trans. Knowl. Data Eng. 29(2), 472–486 (2017)

    Article  Google Scholar 

  24. Dubey, S.R., Singh, S.K., Singh, R.K.: Local bit-plane decoded pattern: a novel feature descriptor for biomedical image retrieval. IEEE J. Biomed. Health Inf. 20(4), 1139–1147 (2016)

    Article  Google Scholar 

  25. Gao, Z., Xue, J.: Member. Democratic diffusion aggregation for image retrieval. IEEE Trans. Multimed. 18(8), 1–1 (2016)

    Article  Google Scholar 

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Acknowledgements

This work was supported by Chongqing Big Data Engineering Laboratory for Children, Chongqing Electronics Engineering Technology Research Center for Interactive Learning, Project of Science and Technology Research Program of Chongqing Education Commission of China. (No. KJZD-K201801601).

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Authors and Affiliations

  1. School of Mathematics and Information Engineering, Chongqing University of Education, Chongqing, China

    Pengcheng Wei

  2. School of Automation, Chongqing University of Posts and Telecommunications, Chongqing, China

    Bo Wang

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  1. Pengcheng Wei
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  2. Bo Wang
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Correspondence to Pengcheng Wei.

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Wei, P., Wang, B. Food image classification and image retrieval based on visual features and machine learning. Multimedia Systems 28, 2053–2064 (2022). https://doi.org/10.1007/s00530-020-00673-6

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