Skip to main content
SpringerLink
Log in

Mobile Image Search: Challenges and Methods

  • Chapter
  • First Online:
Mobile Cloud Visual Media Computing

  • 870 Accesses

Abstract

The proliferation of camera-equipped mobile devices with enhanced mobile computing power and network connectivity results in a rising demand for mobile image search. Although image search has been studied extensively over the last few decades, most existing solutions, developed for desktops and server platforms, are not suitable for mobile devices. In this chapter, we provide an overview of challenging issues unique in mobile search scenarios and present several techniques addressing these challenges. Specifically, we focus the discussion on: (1) robust, distinctive, and fast feature extraction on mobile devices, (2) compact indexing structure for efficient feature matching, and (3) multimodel context-aware data fusion for improving performance of mobile image search.

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

Access this chapter

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
Hardcover Book
USD 54.99
Price excludes VAT (USA)
  • Durable hardcover 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

Institutional subscriptions

References

  1. Yang, X., Zhu, Q., Cheng, K.-T.: Near-duplicate detection for images and videos. In: ACM Workshop on Large Scale Multimedia Retrieval and Mining, Beijing, October 2009

    Google Scholar 

  2. Jegou, H., Douze, M., Schmid, C.: Packing bag-of-features. In: International Conference on Computer Vision, September 2009

    Google Scholar 

  3. Sivic, J., Zisserman, A.: Video Google: a text retrieval approach to object matching in videos. In: Proceedings of International Conference on Computer Vision, vol. 2, pp. 1470–1477 (2003)

    Google Scholar 

  4. Fischler, M.A., Bolles, R.C.: Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commun. ACM 24, 381–395 (1981)

    Article  MathSciNet  Google Scholar 

  5. Chum, O., Matas, J.: Matching with PROSAC—progressive sample consensus. Proc. Comput. Vis. Pattern Recognit. 1, 220–226 (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. Cheng, K.T., Yang, X., Wang, Y.-C.: Performance optimization of vision apps on mobile application processor. In: International Conference on Systems, Signals and Image Processing (IWSSIP), Bucharest, Romania, 7–9 July 2013

    Google Scholar 

  8. Terriberry, T.B., French, L.M., Helmsen, J.: GPU accelerating speeded-up robust features. In: Proceedings of the 3D Data Processing, Visualization and Transmission (2008)

    Google Scholar 

  9. Xie, X., Lu, L., Jia, M.L., Li, H., Seide, F., Ma, W.Y.: Mobile search with multimodel queries. Proc. IEEE 96(4), 589–601 (2008)

    Article  Google Scholar 

  10. Yang, X., Pang, S., Cheng, K.-T.: Mobile image search with multimodel context-aware queries. In: ACM International Workshop on Mobile Vision, June 2010

    Google Scholar 

  11. Rosten, E., Drummond, T.: Machine learning for high speed corner detection. In: Proceedings of the European Conference on Computer Vision (2006)

    Google Scholar 

  12. Bay, H., Ess, A., Tuytelaars, T., Gool, L.V.: SURF: speeded-up robust features. In: Proceedings of the European Conference on Computer Vision (2006)

    Google Scholar 

  13. Bay, H., Ess, A., Tuytelaars, T., Gool, L.V.: Speeded-up robust features. In: Proceedings of the Conference on Vision and Image Understanding, vol. 110(3), June 2008

    Google Scholar 

  14. Yang, X., Cheng, K.T.: Accelerating SURF detector on mobile devices. In: ACM International Conference on Multimedia, Nara, Japan, October 2012

    Google Scholar 

  15. Tuytelaars, T., Mikolajczyk, K.: Local invariant feature detectors: a survey. J. Found. Trends Comput. Graph. Vis. 3, 177–280 (2008)

    Article  Google Scholar 

  16. Rublee, E., Rabaud, V., Konolige, K., Bradski, G.: ORB: an efficient alternative to SIFT or SURF. In: Proceedings of the International Conference on Computer Vision (2011)

    Google Scholar 

  17. Simard, P., Bottou, L., Haffner, P., LeCun, Y.: Boxlets: a fast convolution algorithm for signal processing and neural networks. In: Proceedings of the Neural Information Processing Systems (NIPS) (1998)

    Google Scholar 

  18. Ta, D.N., Chen, W.C., Gelfand, N., Pulli, K.: SURFTrac: efficient tracking and continuous object recognition using local feature descriptors. In: Proceedings of the Conference on Vision and Pattern Recognition (2009)

    Google Scholar 

  19. Rosin, P.L.: Measuring corner properties. J. Comput. Vis. Image Underst. 73(2), 291–307 (1999)

    Article  Google Scholar 

  20. Leutenegger, S., Chli, M., Siegwart, R.: BRISK: binary robust invariant scalable keypoints. In: Proceedins of the Computer Vision on Pattern Recognition (2011)

    Google Scholar 

  21. Alahi, A., Ortiz, R., Vandergheynst, P.: FREAK: fast retinal keypoint. In: Proceedings of the Computer Vision on Pattern Recognition (2012)

    Google Scholar 

  22. Yang, X., Cheng, K.T.: Local difference binary for ultrafast distinctive feature description. IEEE Trans. Pattern Anal. Mach. Intell. 36(1), 188–194 (2014)

    Article  Google Scholar 

  23. Yang, X., Cheng, K.T.: Learning optimized local difference binaries for scalable augmented reality on mobile devices. IEEE Trans. Vis. Comput. Graph. 20(6), 852–865 (2014)

    Article  Google Scholar 

  24. Yang, X., Cheng, K.T.: LDB: an ultrafast feature for scalable augmented reality on mobile device. In: Proceedings of International Symposium on Mixed and Augmented Reality, pp. 49–57 (2012)

    Google Scholar 

  25. CUDA: http://www.nvidia.com/object/cuda_home_new.html

  26. Wang, Y.-C., Cheng, K.-T.: Energy and performance characterization of mobile heterogeneous computing. In: IEEE Workshop on Signal Processing System, Canada, October 2012

    Google Scholar 

  27. Wang, Y.-C., Cheng, K.-T.: Energy-optimized mapping of application to smartphone platform—a case study of mobile face recognition. In: IEEE Workshop of Embedded Computer Vision, USA (2011)

    Google Scholar 

  28. Calonder, M., Lepetit, V., Strecha, C., Fua, P.: BRIEF: binary robust independent elementary features. In: Proceedings of the European Conference on Computer Vision (2010)

    Google Scholar 

  29. GPGPU: http://gpgpu.org/developer

  30. Sinha, S., Frahm, J., Pollefeys, M., Genc, Y.: GPU-based video feature tracking and matching. In: Workshop on Edge Computing Using New Commodity Architectures (2006)

    Google Scholar 

  31. Gionis, A., Indyk, P., Motwani, R.: Similarity search in high dimensions via hashing. In: Proceedings of International Conference on Very Large Databases, pp. 518–529 (1999)

    Google Scholar 

  32. Philbin, J., Chum, O., Isard, M., Sivic, J., Zisserman, A.: Object retrieval with large vocabularies and fast spatial matching. In: Proceedings of Computer Vision and Pattern Recognition, pp. 1–8 (2007)

    Google Scholar 

  33. Wu, Z., Ke, Q.F., Isard, M., Sun, J.: Bundling features for large scale partial-duplicate web image search. In: Proceedings of Computer Vision and Pattern Recognition, pp. 25–32 (2009)

    Google Scholar 

  34. Matas, J., Chum, O., Urban, M., Pajdla, T.: Robust wide baseline stereo from maximally stable extremal regions. In: Proceedings of British Machine Vision Conference, pp. 384–396 (2002)

    Google Scholar 

  35. Lazebnik, S., Schmid, C., Ponce, J.: Beyond bags of features: spatial pyramid matching for recognizing natural scene categories. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 2169–2178 (2006)

    Google Scholar 

  36. Kleban, J., Moxley, E., Xu, J.J., Manjunath, B.S.: Global annotation on georeference photographs. In: ACM International Conference on Image and Video Retrieval, Greece, July 2009

    Google Scholar 

  37. Naimark, L., Foxlin, E.: Circular data matrix fiducial system and robust image processing for a wearable vision-inertial self-tracker. In: Proceedings of the International Symposium on Mixed and Augmented Reality, pp. 27–36 (2002)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Electronics and Information Engineering, Huazhong University of Science and Technology, Wuhan, 430074, Hubei, China

    Xin Yang

  2. Department of Electrical and Computer Engineering, University of California, Santa Barbara, CA, 93106, USA

    K. T. Tim Cheng

Authors
  1. Xin Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. K. T. Tim Cheng
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xin Yang .

Editor information

Editors and Affiliations

  1. Visual Computing Group, Microsoft Research Asia, Beijing, Beijing, China

    Gang Hua

  2. Alibaba Group, Hangzhou, Zhejiang, China

    Xian-Sheng Hua

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this chapter

Cite this chapter

Yang, X., Cheng, K.T.T. (2015). Mobile Image Search: Challenges and Methods. In: Hua, G., Hua, XS. (eds) Mobile Cloud Visual Media Computing. Springer, Cham. https://doi.org/10.1007/978-3-319-24702-1_10

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-24702-1_10

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-24700-7

  • Online ISBN: 978-3-319-24702-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation