Skip to main content
SpringerLink
Log in
Book cover

International Conference on Intelligent Computing

ICIC 2019: Intelligent Computing Theories and Application pp 65–76Cite as

Improving Object Detection by Deep Networks with Class-Related Features

  • Conference paper
  • First Online:

  • 1446 Accesses

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11643))

Abstract

Context is an important additional information for object detection. Class-related features which mainly focus on the coexistence of various classes objects in a single image is a kind of context information. In this paper, we propose an object detection algorithm that makes use of the class-related features. Specifically, in order to improve the performance of object detection, we propose an end-to-end network which introduces the attention mechanism to learn the class-related features and combine it with convolutional neural network (CNN) visual features. The experimental results on PASCAL VOC and MS COCO show that our method get a better performance than the baseline method (Faster R-CNN). The introduction of spatial attention module and channel-wise attention module captures the class-related features greatly. Furthermore, the Gated Recurrent Unit (GRU) which is adopted to combine class-related features and visual features works well in our model.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Alexe, B., Heess, N., Teh, Y.W., Ferrari, V.: Searching for objects driven by context. In: Advances in Neural Information Processing Systems. pp. 881–889 (2012)

    Google Scholar 

  2. Bell, S., Zitnick, C.L., Bala, K., Girshick, R.B.: Inside-outside net: detecting objects in context with skip pooling and recurrent neural networks. In: Computer Vision and Pattern Recognition, pp. 2874–2883 (2016)

    Google Scholar 

  3. Chen, L., et al.: SCA-CNN: spatial and channel-wise attention in convolutional networks for image captioning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5659–5667 (2017)

    Google Scholar 

  4. Cho, K., Van Merrienboer, B., Bahdanau, D., Bengio, Y.: On the properties of neural machine translation: encoder–decoder approaches. In: Empirical Methods in Natural Language Processing, pp. 103–111 (2014)

    Google Scholar 

  5. Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: International Conference on Computer Vision & Pattern Recognition (CVPR 2005), vol. 1, pp. 886–893. IEEE Computer Society (2005)

    Google Scholar 

  6. Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91–110 (2004)

    Article  Google Scholar 

  7. Felzenszwalb, P.F., Girshick, R.B., McAllester, D., Ramanan, D.: Object detection with discriminatively trained part-based models. IEEE Trans. Pattern Anal. Mach. Intell. 32(9), 1627–1645 (2010)

    Article  Google Scholar 

  8. Girshick, R.B.: Fast R-CNN. In: International Conference on Computer Vision, pp. 1440–1448 (2015)

    Google Scholar 

  9. Girshick, R.B., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Computer Vision and Pattern Recognition, pp. 580–587 (2014)

    Google Scholar 

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

    Google Scholar 

  11. Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)

    Article  Google Scholar 

  12. Hoiem, D., Chodpathumwan, Y., Dai, Q.: Diagnosing error in object detectors. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012. LNCS, vol. 7574, pp. 340–353. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-33712-3_25

    Chapter  Google Scholar 

  13. Lienhart, R., Maydt, J.: An extended set of haar-like features for rapid object detection. In: Proceedings. International Conference on Image Processing, vol. 1, p. I. IEEE (2002)

    Google Scholar 

  14. Lin, T.-Y., et al.: Microsoft COCO: common objects in context. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8693, pp. 740–755. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10602-1_48

    Chapter  Google Scholar 

  15. Liu, W., et al.: SSD: single shot multibox detector. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9905, pp. 21–37. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46448-0_2

    Chapter  Google Scholar 

  16. Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks (2015)

    Google Scholar 

  17. Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. international conference on learning representations (2015)

    Google Scholar 

  18. Szegedy, C., et al.: Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–9 (2015)

    Google Scholar 

  19. Uijlings, J.R., Van De Sande, K.E., Gevers, T., Smeulders, A.W.: Selective search for object recognition. Int. J. Comput. Vis. 104(2), 154–171 (2013)

    Article  Google Scholar 

  20. Vedaldi, A., Gulshan, V., Varma, M., Zisserman, A.: Multiple kernels for object detection. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 606–613. IEEE (2009)

    Google Scholar 

  21. Xu, K., et al.: Show, attend and tell: neural image caption generation with visual attention. In: International Conference on Machine Learning, pp. 2048–2057 (2015)

    Google Scholar 

  22. Yang, Z., He, X., Gao, J., Deng, L., Smola, A.: Stacked attention networks for image question answering. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 21–29 (2016)

    Google Scholar 

  23. You, Q., Jin, H., Wang, Z., Fang, C., Luo, J.: Image captioning with semantic attention. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4651–4659 (2016)

    Google Scholar 

  24. Zeiler, M.D., Fergus, R.: Visualizing and understanding convolutional networks. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8689, pp. 818–833. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10590-1_53

    Chapter  Google Scholar 

  25. Zeng, X., Ouyang, W., Yang, B., Yan, J., Wang, X.: Gated bi-directional CNN for object detection. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9911, pp. 354–369. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46478-7_22

    Chapter  Google Scholar 

  26. Zhao, Z., Zheng, P., Xu, S., Wu, X.: Object detection with deep learning: a review. IEEE Trans. Neural Netw. 1–21 (2019)

    Google Scholar 

  27. Zhu, F., Li, H., Ouyang, W., Yu, N., Wang, X.: Learning spatial regularization with image-level supervisions for multi-label image classification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5513–5522 (2017)

    Google Scholar 

  28. Zitnick, C.L., Dollár, P.: Edge boxes: locating object proposals from edges. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8693, pp. 391–405. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10602-1_26

    Chapter  Google Scholar 

Download references

Acknowledgments

This research was supported by the National Natural Science Foundation of China (No. 61672203), and AnHui Natural Science Funds for Distinguished Young Scholar (No. 170808J08).

Author information

Authors and Affiliations

  1. School of Computer Science and Information Engineering, HeFei University of Technology, Hefei, China

    Shoutao Xu & Zhong-Qiu Zhao

Authors
  1. Shoutao Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zhong-Qiu Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Shoutao Xu .

Editor information

Editors and Affiliations

  1. Tongji University, Shanghai, China

    De-Shuang Huang

  2. Polytechnic University of Bari, Bari, Italy

    Vitoantonio Bevilacqua

  3. University of Wollongong, North Wollongong, NSW, Australia

    Prashan Premaratne

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Xu, S., Zhao, ZQ. (2019). Improving Object Detection by Deep Networks with Class-Related Features. In: Huang, DS., Bevilacqua, V., Premaratne, P. (eds) Intelligent Computing Theories and Application. ICIC 2019. Lecture Notes in Computer Science(), vol 11643. Springer, Cham. https://doi.org/10.1007/978-3-030-26763-6_7

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-26763-6_7

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-26762-9

  • Online ISBN: 978-3-030-26763-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation