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Commodity Image Retrieval Based on Convolutional Neural Network and Late Fusion

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Proceedings of the International Conference on Artificial Intelligence and Computer Vision (AICV2021) (AICV 2021)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1377))

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Abstract

Commodity image retrieval has great research value in the field of e-commerce. However, at the same time, the diversity of commodity images makes the effectiveness of commodity retrieval often unsatisfactory. For image retrieval, the question of whether a given query image is the best query result for the underlying framework using a convolutional neural network as a feature extractor is often neglected. Inspired by the superiority of deep learning in image content understanding and powerful image feature extraction, this paper proposes a new deep learning-based framework for commodity image retrieval to make commodity image retrieval have a better retrieval effect. The framework achieves an efficient retrieval of images by combining deep Convolutional Neural Networks for the late fusion of images at the scoring level. A database of 35 categories of commodities containing 3500 images was created for experimental validation in this paper. Experiments comparing the performance between the frameworks using our dataset show that this paper’s proposed framework has higher retrieval accuracy than the base framework.

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References

  1. Smeulders, A.W.M., et al.: Content-based image retrieval at the end of the early years. IEEE Trans. Pattern Anal. Mach. Intell. 22(12), 1349–1380 (2000). https://doi.org/10.1109/34.895972

    Article  Google Scholar 

  2. Bach, J.R., Fuller, C., Gupta, A., et al.: Virage image search engine: an open framework for image management. In: Storage & Retrieval for Still Image & Video Databases IV. International Society for Optics and Photonics (1996)

    Google Scholar 

  3. Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: IEEE Computer Society Conference on Computer Vision & Pattern Recognition. IEEE (2005)

    Google Scholar 

  4. Bay, H., Tuytelaars, T., Gool, L.V.: SURF: speeded up robust features. In: Proceedings of the 9th European Conference on Computer Vision - Volume Part I. Springer (2006)

    Google Scholar 

  5. Lin, X., Gokturk, B., Sumengen, B., et al.: Visual search engine for product images. Proc. Spie 6820, 22 (2008)

    Google Scholar 

  6. Fang, Q.: Content-Based Commodity Image Retrieval. Nanjing University of Science and Technology (2013)

    Google Scholar 

  7. Sa, L.: Application of Content-Based Image Retrieval Technology in Multi-category Commodity Image Retrieval. The Dalian University of Technology

    Google Scholar 

  8. Krizhevsky, A., Sutskever, I., Hinton, G.: ImageNet classification with deep convolutional neural networks. In: NIPS. Curran Associates Inc. (2012)

    Google Scholar 

  9. Razavian, A.S., Azizpour, H., Sullivan, J., et al.: CNN features off-the-shelf: an astounding baseline for recognition (2014)

    Google Scholar 

  10. Babenko, A., Slesarev, A., Chigorin, A., et al.: Neural codes for image retrieval. In: European Conference on Computer Vision. Springer (2014)

    Google Scholar 

  11. Lin, K., Yang, H.F., Liu, K.H., et al.: Rapid clothing retrieval via deep learning of binary codes and hierarchical search. In: ACM on International Conference on Multimedia Retrieval. ACM (2015)

    Google Scholar 

  12. Kiapour, M.H., Han, X., Lazebnik, S., et al.: Where to buy it: matching street clothing photos in online shops. In: IEEE International Conference on Computer Vision. IEEE (2015)

    Google Scholar 

  13. Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. Computerence (2014)

    Google Scholar 

  14. Pan, S.J., Yang, Q.: A survey on transfer learning. IEEE Trans. Knowl. Data Eng. 22(10), 1345–1359 (2010)

    Article  Google Scholar 

  15. Zheng, L., Wang, S., Tian, L., et al.: Query-adaptive late fusion for image search and person re-identification. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE (2015)

    Google Scholar 

  16. Alkoot, F.M., Kittler, J.: Experimental evaluation of expert fusion strategies. Pattern Recogn. Lett. 20(11/13), 1361–1369 (1999)

    Article  Google Scholar 

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Acknowledgment

This paper is supported by the Heilongjiang Provincial Natural Science Foundation of China(LH2020F008).

Author information

Authors and Affiliations

  1. Heilongjiang Provincial Key Laboratory of Electronic Commerce and Information Processing, Harbin University of Commerce, Harbin, China

    Zhijie Zhao, Hongjie Zhang, Huadong Sun & Baoxing Qiao

  2. School of Computer and Information Engineering, Harbin University of Commerce, Harbin, China

    Zhijie Zhao, Hongjie Zhang, Huadong Sun & Baoxing Qiao

Authors
  1. Zhijie Zhao
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  2. Hongjie Zhang
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  3. Huadong Sun
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  4. Baoxing Qiao
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Editor information

Editors and Affiliations

  1. Information Technology Department, Faculty of Computers and Artificial Intelligence, Cairo University, Giza, Egypt

    Aboul Ella Hassanien

  2. Faculty of Sciences and Techniques, Hassan 1st University, Settat, Morocco

    Abdelkrim Haqiq

  3. School of Medicine, University of Missouri, Columbia, MO, USA

    Peter J. Tonellato

  4. ISAE-ENSMA, Futuroscope Chasseneuil Cedex, France

    Ladjel Bellatreche

  5. British University Vietnam, Hung Yen, Vietnam

    Sam Goundar

  6. Faculty of Computers and Artificial Intelligence, Benha University, Benha, Egypt

    Ahmad Taher Azar

  7. NEST Research Group, ENSEM, Hassan II University of Casablanca, Casablanca, Morocco

    Essaid Sabir

  8. High National School for Computer Science and Systems Analysis (ENSIAS), Mohammed V University, Rabat, Morocco

    Driss Bouzidi

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Zhao, Z., Zhang, H., Sun, H., Qiao, B. (2021). Commodity Image Retrieval Based on Convolutional Neural Network and Late Fusion. In: Hassanien, A.E., et al. Proceedings of the International Conference on Artificial Intelligence and Computer Vision (AICV2021). AICV 2021. Advances in Intelligent Systems and Computing, vol 1377. Springer, Cham. https://doi.org/10.1007/978-3-030-76346-6_9

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