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CAAI International Conference on Artificial Intelligence

CICAI 2022: Artificial Intelligence pp 229–241Cite as

Image Sampling for Machine Vision

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13604))

Abstract

Image sampling is one of the basic methods for image compression, which is efficient for image store, transmission, and applications. Existing sampling methods are designed for human-eye perception, which discard unconcerned information to decrease the amount of data considering the visual preference of human. However, these methods cannot adapt to the increasing machine vision tasks since there is a lot of redundant information for machine analysis to ensure the comfort of human eyes. In this paper, we propose an image sampling method for machine vision. We adopt a gray image to retain the main structural information of the image, and construct a concise color feature map based on the dominant channel of pixels to provide color information. Experiments on public datasets including COCO and ImageNet show that our sampling method can adapt to the characteristics of machine vision and greatly reduce the amount of data with little impact on the performance of mainstream computer vision algorithms.

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References

  1. Nyquist, H.: Certain topics in telegraph transmission theory. Proc. IEEE 90(2), 280–305 (1928)

    Article  Google Scholar 

  2. Terzopoulos D., Vasilescu M.: Sampling and reconstruction with adaptive meshes. In Computer Vision and Pattern Recognition, pp. 70–75 (1991)

    Google Scholar 

  3. Eldar, Y., Lindenbaum, M., et al.: The farthest point strategy for progressive image sampling. IEEE Trans. Image Process. 6(9), 1305–1315 (1997)

    Article  Google Scholar 

  4. Ramoni, G., Carrato, S.: An adaptive irregular sampling algorithm and its application to image coding. Image Vision Comput. 19(7), 451–460 (2001)

    Article  Google Scholar 

  5. Wei, L., Wang, R.: Differential domain analysis for non-uniform sampling. ACM Trans. Graph. 30(4), 1–10 (2011)

    Article  Google Scholar 

  6. Marvasti, F., Liu, C., Adams, G.: Analysis and recovery of multidimensional signals from irregular samples using nonlinear and iterative techniques. Sig. Process 36, 13–30 (1994)

    Article  MATH  Google Scholar 

  7. Chen, W., Itoh, S., Shiki, J.: Irregular sampling theorems for wavelet subspace. IEEE Trans. Inf. Theory 44(3), 1131–1142 (1998)

    Article  MathSciNet  MATH  Google Scholar 

  8. Liu, Y.: Irregular sampling for spline wavelet. IEEE Trans. Inf. Theory 42(2), 623–627 (1996)

    Article  MathSciNet  MATH  Google Scholar 

  9. Oztireli, A., Alexa, M., Gross, M.: Spectral sampling of manifolds. AMC Trans. Grap. 29(6), 1–8 (2010)

    Article  Google Scholar 

  10. Devir, Z., Lindenbaum, M.: Blind adaptive sampling of images. IEEE Trans. Image Process. 21(4), 1478–1487 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  11. Sochen, N., Kimmel, R., Malladi, R.: A general framework for low level vision. IEEE Trans. Image Process. 7(3), 310–318 (1998)

    Article  MathSciNet  MATH  Google Scholar 

  12. Cheng, S., Dey, T., Ramos, E.: A manifold reconstruction from point samples. In: Proceedings of the Sixteenth Annual ACM-SIAM Symposium on Discrete Algorithms, pp. 1018–1027 (2005)

    Google Scholar 

  13. Saucan, E., Appleboime, E., Zeevi, Y.: Geometric approach to sampling and communication. Sampl. Theory Sig. Image Process. 11, 1 (2010)

    MathSciNet  Google Scholar 

  14. Vipula, S., Navin, R.: Data Compression using non-uniform sampling. In: International Conference on Signal Processing, pp. 603–607 (2007)

    Google Scholar 

  15. Ji, S., Xue, Y., Lawrence, C.: Bayesian compressive sensing. IEEE Trans. Sig. Process. 56(6), 2346–2356 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  16. Bahzad, S., Nazanin, R.: Model-based nonuniform compressive sampling and recovery of natural images utilizing a wavelet-domain universal hidden Markov model. IEEE Trans. Sig. Process. 65(1), 95–104 (2017)

    Article  MathSciNet  MATH  Google Scholar 

  17. Matthew, M., Robert, N.: Near-optimal adaptive compressed sensing. IEEE Trans. Inf. Theory 60(7), 4001–4012 (2014)

    Google Scholar 

  18. Ali, T., Farokh, M.: Adaptive sparse image sampling and recovery. IEEE Trans. Comput. Imaging 4(3), 311–325 (2018)

    Google Scholar 

  19. Dai, Q., Henry, C., et al.: Adaptive image sampling using deep learning and its application on X-ray fluorescence image reconstruction. IEEE Trans. Multim. 22(10), 2564–2578 (2020)

    Google Scholar 

  20. William, P., Joan, M.: JPEG: Still image data compression standard. ISBN: 978–0442012724

    Google Scholar 

  21. Wang, Z., Li, F., Xu, J., Pamela, C.: Human-machine interaction oriented image coding for resource-constrained visual monitoring in IoT. IEEE Intern. Things J. (2022)

    Google Scholar 

  22. Mei, Y., Li, L., Li, Z., Li, F.: Learning-based scalable image compression with latent-feature reuse and prediction. IEEE Trans. Multim. 24, 4143–4157 (2022)

    Article  Google Scholar 

  23. Jia, D., Wei, D., Li, F., et al.: ImageNet: a large-scale hierarchical image database. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 248–255 (2009)

    Google Scholar 

  24. Lin, T., Marie, M., et al.: Microsoft COCO: common objects in context. In: European Conference on Computer Vision, pp. 740–755 (2014)

    Google Scholar 

  25. Itti, L., Koch, C., Niebur, E.: A model of saliency-based visual attention for rapid scene analysis. IEEE Trans. Pattern Anal. Mach. Intell. 20(11), 1254–1259 (1998)

    Article  Google Scholar 

  26. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)

    Google Scholar 

  27. Joseph, R., Santosh, D., Ross, G., Ali, F.: You only look once: unified, real-time object detection. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 779–788 (2016)

    Google Scholar 

  28. Joseph, R., Ali, F.: YOLO9000: better, faster, stronger. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 6517–6525 (2017)

    Google Scholar 

  29. Joseph, R., Ali, F.: YOLOv3: An incremental improvement. https://arxiv.org/pdf/1804.02767.pdf

  30. Glenn J., et al.: YOLOv5 homepage. https://github.com/ultralytics/yolov5

  31. Ren, S., He, K., Ross, G., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans. Pattern Analysis Mach. Intell. 39(6), 1137–1149 (2017)

    Article  Google Scholar 

  32. Zhou, X., Wang, D., Philipp, K.: Objects as points. https://arxiv.org/pdf/1904.07850.pdf

  33. Mark, S., Andrew, H., et al.: MobileNetV2: inverted residuals and linear bottlenecks. https://arxiv.org/pdf/1801.04381.pdf

  34. Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: International Conference on Learning Representation, pp. 1–14 (2015)

    Google Scholar 

  35. Huang, G., Liu, Z., et al.: Densely connected convolutional networks. IEEE Conference on Computer Vision and Pattern Recognition, pp. 2261–2269 (2017)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Xi’an Jiaotong University, Xi’an, 710049, China

    Jiashuai Cui & Fan Li

  2. Xinjiang University, Urumqi, 830046, China

    Liejun Wang

Authors
  1. Jiashuai Cui
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  2. Fan Li
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  3. Liejun Wang
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Corresponding author

Correspondence to Fan Li .

Editor information

Editors and Affiliations

  1. Tsinghua University, Beijing, China

    Lu Fang

  2. Xiaomi Inc., Beijing, China

    Daniel Povey

  3. Shanghai Jiao Tong University, Shanghai, China

    Guangtao Zhai

  4. JD Explore Academy, Beijing, China

    Tao Mei

  5. Chinese Academy of Sciences, Beijing, China

    Ruiping Wang

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© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

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Cite this paper

Cui, J., Li, F., Wang, L. (2022). Image Sampling for Machine Vision. In: Fang, L., Povey, D., Zhai, G., Mei, T., Wang, R. (eds) Artificial Intelligence. CICAI 2022. Lecture Notes in Computer Science(), vol 13604. Springer, Cham. https://doi.org/10.1007/978-3-031-20497-5_19

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  • DOI: https://doi.org/10.1007/978-3-031-20497-5_19

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-20496-8

  • Online ISBN: 978-3-031-20497-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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