Skip to main content
Springer Nature Link
Log in

Sketch-Based Image Retrieval with Multiple Binary HoG Descriptor

  • Conference paper
  • First Online:
Internet Multimedia Computing and Service (ICIMCS 2017)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 819))

Included in the following conference series:

  • 1501 Accesses

Abstract

The proliferation of touch screen devices has enabled Sketch-Based Image Retrieval (SBIR) to become an effective method for image retrieval. Although prior research efforts have ex-tensively explored the methods for SBIR, the appropriate descriptor which can accurately and efficiently describe sketch and natural images is still unavailable. To further improve the accuracy and efficiency of SBIR, in this paper, we pro-pose a novel sketch-based image retrieval method leveraging Multiple Binary HoG (MBHoG) descriptor. In this method, two novel binary descriptors named Primary Binary HoG (PBHoG) and Discrete Binary HoG (DBHoG), are proposed and combined, together with the color feature as an extra condition to enhance the accuracy of results. We use Ham-ming distance with two binary masks as constraint to re-trieve images. The method ensures time and space efficiency. The experimental results performed on the public dataset demonstrate that the proposed method has the superiority of accuracy.

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

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Tang, J., Li, Z., Wang, M., Zhao, R.: Neighborhood discriminant hashing for large-scale image retrieval. IEEE Trans. Image Process. 24(9), 2827–2840 (2015)

    Article  MathSciNet  Google Scholar 

  2. Hu, R., Barnard, M., Collomosse, J.: Gradient field descriptor for sketch based retrieval and localization. In: IEEE International Conference on Image Processing, pp. 1025–1028 (2010)

    Google Scholar 

  3. Hu, R., Collomosse, J.: A performance evaluation of gradient field hog descriptor for sketch based image retrieval. Comput. Vis. Image Underst. 117(7), 790–806 (2013)

    Article  Google Scholar 

  4. Tu, B., Collomosse, J.: Scalable sketch-based image retrieval using color gradient features. In: IEEE International Conference on Computer Vision Workshop, pp. 1012–1019 (2016)

    Google Scholar 

  5. Eitz, M., Hildebrand, K., Boubekeur, T., Alexa, M.: A descriptor for large scale image retrieval based on sketched feature lines. In: Eurographics Symposium on Sketch-Based Interfaces and Modeling, pp. 29–36 (2009)

    Google Scholar 

  6. Lin, Y.L., Hsu, W.H.: Sketch-based image retrieval on mobile devices using compact hash bits. In: ACM International Conference on Multimedia, pp. 913–916 (2012)

    Google Scholar 

  7. Zhou, R., Chen, L., Zhang, L.: Sketch-based image retrieval on a large scale database. In: ACM International Conference on Multimedia, pp. 973–976 (2012)

    Google Scholar 

  8. Fu, H., Zhao, H., Kong, X., Zhang, X.: BHoG: binary descriptor for sketch-based image retrieval. Multimed. Syst. 22(1), 127–136 (2016)

    Article  Google Scholar 

  9. Eitz, M., Hildebrand, K., Boubekeur, T., Alexa, M.: Sketch-based image retrieval: benchmark and bag-of-features descriptors. IEEE Trans. Visual Comput. Graph. 17(11), 1624–1636 (2011)

    Article  Google Scholar 

  10. Cao, Y., Wang, C., Zhang, L., Zhang, L.: Edgel index for large-scale sketch-based image search. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 761–768 (2011)

    Google Scholar 

  11. Aarthi, R., Amudha, J.: Saliency based modified chamfers matching method for sketch based image retrieval. In: International Conference on Innovations in Information, Embedded and Communication Systems, pp. 1–4 (2015)

    Google Scholar 

  12. Cao, Y., Wang, H., Wang, C., Li, Z., Zhang, L., Zhang, L.: MindFinder: interactive sketch-based image search on millions of images. In: International Conference on Multimedia 2010, Firenze, Italy, pp. 1605–1608, October 2010

    Google Scholar 

  13. Chen, T., Cheng, M.M., Tan, P., Shamir, A., Hu, S.M.: Sketch2Photo: internet image montage. ACM Trans. Graph. 28(5), 1–10 (2009)

    Google Scholar 

  14. Eitz, M., Hildebrand, K., Boubekeur, T., Alexa, M.: PhotoSketch: a sketch based image query and compositing system. In: SIGGRAPH 2009: Talks, p. 60 (2009)

    Google Scholar 

  15. Dollar, P., Zitnick, C.L.: Fast edge detection using structured forests. IEEE Trans. Pattern Anal. Mach. Intell. 37(8), 1558–1570 (2015)

    Article  Google Scholar 

  16. Li, Z., Liu, J., Yang, Y., Zhou, X., Lu, H.: Clustering-guided sparse structural learning for unsupervised feature selection. IEEE Trans. Knowl. Data Eng. 26(9), 2138–2150 (2014)

    Article  Google Scholar 

  17. Won, C.S., Park, D.K., Park, S.J.: Efficient use of MPEG7 edge histogram descriptor. ETRI J. 24(1), 23–30 (2002)

    Article  Google Scholar 

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

    Google Scholar 

Download references

Acknowledgement

This work was partially supported by the National Natural Science Foundation of China (Grant No. 61522203 and 61572252), the Natural Science Foundation of Jiangsu Province (Grant No. BK20140058 and BK20150755).

Author information

Authors and Affiliations

  1. Nanjing University of Science and Technology, Nanjing, China

    Tianqi Wang & Jinhui Tang

  2. Nanjing University of Aeronautics and Astronautics, Nanjing, China

    Liyan Zhang

Authors
  1. Tianqi Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Liyan Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jinhui Tang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Liyan Zhang .

Editor information

Editors and Affiliations

  1. Multimedia Communications Department, EURECOM, Sophia Antipolis, France

    Benoit Huet

  2. Shandong University , Qingdao, China

    Liqiang Nie

  3. Hefei University of Technology , Hefei, China

    Richang Hong

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Wang, T., Zhang, L., Tang, J. (2018). Sketch-Based Image Retrieval with Multiple Binary HoG Descriptor. In: Huet, B., Nie, L., Hong, R. (eds) Internet Multimedia Computing and Service. ICIMCS 2017. Communications in Computer and Information Science, vol 819. Springer, Singapore. https://doi.org/10.1007/978-981-10-8530-7_4

Download citation

  • DOI: https://doi.org/10.1007/978-981-10-8530-7_4

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-8529-1

  • Online ISBN: 978-981-10-8530-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics