Skip to main content
SpringerLink
Log in

Real-time indexing for large image databases: color and edge directivity descriptor on GPU

  • Published:
The Journal of Supercomputing Aims and scope Submit manuscript

Abstract

In this paper, we focus on implementing the extraction of a well-known low-level image descriptor using the multicore power provided by general-purpose graphic processing units (GPGPUs). The color and edge directivity descriptor, which incorporates both color and texture information achieving a successful trade-off between effectiveness and efficiency, is employed and reassessed for parallel execution. We are motivated by the fact that image/frame indexing should be achieved real time, which in our case means that a system should be capable of indexing a frame or an image as it becomes part of a database (ideally, calculating the descriptor as the images are captured). Two strategies are explored to accelerate the method and bypass resource limitations and architectural constrains. An approach that exclusively uses the GPU together with a hybrid implementation that distributes the computations to both available GPU and CPU resources are proposed. The first approach is strongly based on the compute unified device architecture and excels compared to all other solutions when the GPU resources are abundant. The second implementation suggests a hybrid scheme where the extraction process is split in two sequential stages, allowing the input data (images or video frames) to be pipelined through the central and the graphic processing units. Experimental results were conducted on four different combinations of GPU–CPU technologies in order to highlight the strengths and the weaknesses of all implementations. Real-time indexing is obtained over all computational setups for both GPU-only and Hybrid techniques. An impressive 22 times acceleration is recorded for the GPU-only method. The proposed Hybrid implementation outperforms the GPU-only implementation and becomes the preferred solution when a low-cost setup (i.e., more advanced CPU combined with a relatively weak GPU) is employed.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  1. Datta R, Joshi D, Li J, Wang JZ (2008) Image retrieval: ideas, influences, and trends of the new age. ACM Comput Surv 40(2):5:1–5:60

    Article  Google Scholar 

  2. Wetzel A (1997) Computational aspects of pathology image classification and retrieval. J Supercomput 11(3):279–293

    Article  Google Scholar 

  3. Ren R, Collomosse J, Jose J (2011) A bovw based query generative model. In: Proceedings of the 17th international conference on advances in multimedia modeling. Volume Part I, ser. MMM’11, 2011, pp 118–128

  4. Lux M, Chatzichristofis S (2008) Lire: lucene image retrieval: an extensible java cbir library. In: Proceeding of the 16th ACM international conference on multimedia. ACM, 2008, pp 1085–1088

  5. Chatzichristofis S, Iakovidou C, Boutalis Y, Marques O (2013) Co.vi.wo.: color visual words based on non-predefined size codebooks. IEEE Trans Cybernet 43(1):192–205

    Article  Google Scholar 

  6. Lazebnik S, Schmid C, Ponce J (2006) Beyond bags of features: spatial pyramid matching for recognizing natural scene categories. CVPR 2:2169–2178

    Google Scholar 

  7. Zagoris K, Chatzichristofis SA, Arampatzis A (2011) Bag-of-visual-words vs global image descriptors on two-stage multimodal retrieval. In: Proceedings of the 34th international ACM SIGIR conference on research and development in information Retrieval, pp 1251–1252

  8. Amanatiadis A, Kaburlasos V, Gasteratos A, Papadakis S (2011) Evaluation of shape descriptors for shape-based image retrieval. IET Image Process 5(5):493–499

    Article  Google Scholar 

  9. Sevilla J, Bernabe S, Plaza A (2014) Unmixing-based content retrieval system for remotely sensed hyperspectral imagery on GPUs. J Supercomput, pp 1–12

  10. Park IK, Singhal N, Lee MH, Cho S, Kim CW (2011) Design and performance evaluation of image processing algorithms on gpus. IEEE Trans Parallel Distrib Syst 22(1):91–104

    Article  Google Scholar 

  11. Antikainen J, Havel J, Josth R, Herout A, Zemcík P, Hauta-Kasari M, Zemcík P (2011) Nonnegative tensor factorization accelerated using GPGPU. IEEE Trans Parallel Distrib Syst 22(7):1135–1141

    Article  Google Scholar 

  12. Zhu L, Jin H, Zheng R, Feng X (2013) Effective naive bayes nearest neighbor based image classification on GPU. J Supercomput, pp 1–29

  13. Risojević V, Babić Z, Dobravec T, Bulić P et al (2013) A GPU implementation of a structural-similarity-based aerial-image classification. J Supercomput 65(2):978–996

    Article  Google Scholar 

  14. van de Sande KEA, Gevers T, Snoek CGM (2011) Empowering visual categorization with the GPU. IEEE Trans Multimed 13(1):60–70

    Article  Google Scholar 

  15. Alvarado R, Tapia JJ, Rolón C (2013) Medical image segmentation with deformable models on graphics processing units. J Supercomput, pp 1–26

  16. Song B, Tang W, Nguyen T-D, Hassan MM, Huh EN (2013) An optimized hybrid remote display protocol using GPU-assisted m-JPEG encoding and novel high-motion detection algorithm. J Supercomput 66(3):1729–1748

    Article  Google Scholar 

  17. López MB, Nykänen H, Hannuksela J, Silvén O, Vehviläinen M (2011) Accelerating image recognition on mobile devices using GPGPU. In:Proceedings of SPIE 7872:78720R

  18. Amanatiadis A, Bampis L, Gasteratos A (2014) Accelerating image super-resolution regression by a hybrid implementation in mobile devices. In: Proceedings IEEE international conference on consumer electronics, pp 335–336

  19. Nalpantidis L, Amanatiadis A, Sirakoulis G, Gasteratos A (2011) Efficient hierarchical matching algorithm for processing uncalibrated stereo vision images and its hardware architecture. IET Image Process. 5(5):481–492

    Article  Google Scholar 

  20. Chatzichristofis S, Zagoris K, Boutalis Y, Papamarkos N (2010) Accurate image retrieval based on compact composite descriptors and relevance feedback information. Int J Pattern Recogn Artif Intell 24(2):207–244

    Article  Google Scholar 

  21. Jiang Y, Xu X, Terlecky P, Abdelzaher T, Bar-Noy A, Govindan R (2013) Mediascope: selective on-demand media retrieval from mobile devices. In: Proceedings of the 12th international conference on information processing in sensor networks, ser. IPSN ’13. New York, NY, USA: ACM, 2013, pp 289–300

  22. Zha Z-J, Tian Q, Cai J, Wang Z (2013) Interactive social group recommendation for flickr photos. Neurocomputing 105:30–37

    Article  Google Scholar 

  23. van Leuken RH, Pueyo LG, Olivares X, van Zwol R (2009) Visual diversification of image search results. In: WWW. ACM, 2009, pp 341–350

  24. Jin X, Gallagher AC, Cao L, Luo J, Han J (2010) The wisdom of social multimedia: using flickr for prediction and forecast. In: ACM Multimedia, 2010, pp 1235–1244

  25. Daras P, Semertzidis T, Makris L, Strintzis MG (2010) Similarity content search in content centric networks. In: ACM multimedia, 2010, pp 775–778

  26. Iakovidou C, Anagnostopoulos N, Kapoutsis AC, Boutalis YS, Chatzichristofis SA (2014) Searching images with MPEG-7 ( & mpeg-7-like) powered localized descriptors: the SIMPLE answer to effective content based image retrieval. In 2014 12th International workshop on content-based multimedia indexing (CBMI), Klagenfurt, Austria, June 18–20(2014), 2014, pp 1–6. [Online]. doi:10.1109/CBMI.2014.6849821

  27. Lux M, Marques O, Schoffmann K, Boszormenyi L, Lajtai G (2010) A novel tool for summarization of arthroscopic videos. Multimed Tools Appl 46(2–3):521–544

    Article  Google Scholar 

  28. Rafailidis D, Manolopoulou S, Daras P (2013) A unified framework for multimodal retrieval. Pattern Recogn 46(12):3358–3370

    Article  Google Scholar 

  29. Piras L, Giacinto G (2012) Synthetic pattern generation for imbalanced learning in image retrieval. Pattern Recogn Lett 33(16):2198–2205

    Article  Google Scholar 

  30. Vallet D, Cantador I, Jose JM (2013) Exploiting semantics on external resources to gather visual examples for video retrieval. Int J Multimed Inf Retriev 2(2):117–130

    Article  Google Scholar 

  31. Daras P, Manolopoulou S, Axenopoulos A (2012) Search and retrieval of rich media objects supporting multiple multimodal queries. IEEE Trans Multimed 14(3–2):734–746

    Article  Google Scholar 

  32. Yu J, Jin X, Han J, Luo J (2011) Collection-based sparse label propagation and its application on social group suggestion from photos. ACM TIST 2(2):12

    Google Scholar 

  33. Chatzichristofis S, Boutalis Y (2008) CEDD: color and edge directivity descriptor: a compact descriptor for image indexing and retrieval. LNCS, Computer Vision Systems

    Google Scholar 

  34. Wang J, Li J, Wiederhold G (2001) Simplicity: semantics-sensitive integrated matching for picture libraries. IEEE Transactions on pattern analysis and machine intelligence, pp 947–963

  35. Schaefer G, Stich M (2004) UCID-an uncompressed colour image database. Storage and retrieval methods and applications for multimedia 2004, vol 5307, pp 472–480

  36. Chatzichristofis S, Boutalis Y (2010) Content based radiology image retrieval using a fuzzy rule based scalable composite descriptor. Multimed Tools Appl 46:493–519

    Article  Google Scholar 

  37. Chatzichristofis S, Arampatzis A, Boutalis Y (2010) Investigating the behavior of compact composite descriptors in early fusion, late fusion and distributed image retrieval. Radioengineering 19(4):725

    Google Scholar 

  38. Chatzichristofis SA, Boutalis YS, Lux M (2010) SpCD–spatial color distribution descriptor. A fuzzy rule based compact composite descriptor appropriate for hand drawn color sketches retrieval. In: ICAART, 2010, pp 58–63

  39. Manjunath B, Ohm J, Vasudevan V, Yamada A (2001) Color and texture descriptors. IEEE Trans Circuits Syst video Technol 11(6):703–715

    Article  Google Scholar 

  40. Huang J, Kumar S, Mitra M, Zhu W (2001) Image indexing using color correlograms. US Patent 6,246,790, 12, pp 1–16

  41. Thomee B, Bakker EM, Lew MS (21010) Top-surf: a visual words toolkit. In ACM multimedia, 2010, pp 1473–1476

  42. Sartori J, Kumar R (2013) Branch and data herding: reducing control and memory divergence for error-tolerant gpu applications. IEEE Trans Multimed 15(2):279–290

    Article  Google Scholar 

  43. van der Laan WJ, Jalba AC, Roerdink JB (2011) Accelerating wavelet lifting on graphics hardware using CUDA. IEEE Trans Parallel Distrib Syst 22(1):132–146

    Article  Google Scholar 

  44. Li R, Saad Y (2013) Gpu-accelerated preconditioned iterative linear solvers. J Supercomput 63(2):443–466

    Article  Google Scholar 

  45. Thibault JC, Senocak I (2012) Accelerating incompressible flow computations with a pthreads-CUDA implementation on small-footprint multi-GPU platforms. J Supercomput 59(2):693–719

    Article  Google Scholar 

  46. Cano A, Luna JM, Ventura S (2013) High performance evaluation of evolutionary-mined association rules on GPUS. J Supercomput 66(3):1438–1461

    Article  Google Scholar 

Download references

Acknowledgments

This research has been co-financed by the European Union (European Social Fund-ESF) and Greek national funds through the Operational Program “Education and Lifelong Learning” of the National Strategic Reference Framework (NSRF), Research Funding Program: Heracleitus II. Investing in knowledge society through the European Social Fund.

Author information

Authors and Affiliations

  1. Department of Electrical and Computer Engineering, Democritus University of Thrace, 12 Vas. Sofias Str, Xanthi, 67100, Greece

    L. Bampis, C. Iakovidou, S. A. Chatzichristofis, Y. S. Boutalis & A. Amanatiadis

Authors
  1. L. Bampis
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. C. Iakovidou
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. S. A. Chatzichristofis
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Y. S. Boutalis
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. A. Amanatiadis
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to A. Amanatiadis.

Appendix

Appendix

The following Tables 5, 6, 7, and 8 are illustrating the step-by-step timing (in seconds for 1,000 images) of the GPU-only implementation.

Table 5 Step-by-step times obtained by Setup1
Full size table
Table 6 Step-by-step times obtained by Setup2
Full size table
Table 7 Step-by-step times obtained by Setup3
Full size table
Table 8 Step-by-step times obtained by Setup4
Full size table

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Bampis, L., Iakovidou, C., Chatzichristofis, S.A. et al. Real-time indexing for large image databases: color and edge directivity descriptor on GPU. J Supercomput 71, 909–937 (2015). https://doi.org/10.1007/s11227-014-1343-2

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11227-014-1343-2

Keywords

Search

Navigation