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On-chip real-time feature extraction using semantic annotations for object recognition

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Abstract

Describing image features in a concise and perceivable manner is essential to focus on candidate solutions for classification purpose. In addition to image recognition with geometric modeling and frequency domain transformation, this paper presents a novel 2D on-chip feature extraction named semantics-based vague image representation (SVIR) to reduce the semantic gap of content-based image retrieval. The development of SVIR aims at successively deconstructing object silhouette into intelligible features by pixel scans and then evolves and combines piecewise features into another pattern in a linguistic form. In addition to semantic annotations, SVIR is free of complicated calculations so that on-chip designs of SVIR can attain real-time processing performance without making use of a high-speed clock. The effectiveness of SVIR algorithm was demonstrated with timing sequences and real-life operations based on a field-programmable-gate-array (FPGA) development platform. With low hardware resource consumption on a single FPGA chip, the design of SVIR can be used on portable machine vision for ambient intelligence in the future.

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References

  1. Bezdek, J.C., Keller, J., Krisnapuram, R., Pal, N.R.: Fuzzy models and algorithms for pattern recognition and image processing. Springer (2005)

  2. Nixon, M.S., Aguado, A.S.: Feature extraction and image processing. Academic Press, London (2008)

    Google Scholar 

  3. Treiber, M.: An introduction to object recognition. Springer, New York (2010)

    Book  MATH  Google Scholar 

  4. Li, S.Z., Jain, A.K.: Handbook of face recognition. Springer, New York (2011)

    Book  MATH  Google Scholar 

  5. Arth, C., Bischof, H.: Real-time object recognition using local features on a DSP-based embedded system. J. Real Time Image Proc. 3, 233–253 (2008)

    Article  Google Scholar 

  6. Lin, C.T., Yeh, C.M., Liang, S.F., Chung, J.F., Kumar, N.: Support-vector-based fuzzy neural network for pattern classification. IEEE Trans. Fuzzy Syst. 14(1), 31–41 (2006)

    Article  Google Scholar 

  7. Espejo, P.G., Ventura, S., Herrera, F.: A survey on the application of genetic programming to classification. IEEE Trans. Syst. Man Cybern. Part C Appl. Rev. 40(2), 121–144 (2010)

    Article  Google Scholar 

  8. Berretti, S., Bimbo, A.D., Pala, P.: Retrieval by shape similarity with perceptual distance and effective indexing. IEEE Trans. Multimed. 2(4), 225–239 (2000)

    Article  Google Scholar 

  9. Shotton, J., Black, A., Cipolla, R.: Multi-scale categorical object recognition using contour fragments. IEEE Trans. Pattern Anal. Mach. Intell. 30(7), 1270–1281 (2008)

    Article  Google Scholar 

  10. Chung, Y., Prasanna, V.K.: Parallelizing image feature extraction on coarse-grain machines. IEEE Trans. Pattern Anal. Mach. Intell. 20(12), 1389–1394 (1998)

    Article  Google Scholar 

  11. Abbasi, S., Mokhtarian, F., Kittler, J.: Curvature scale space image in shape similarity retrieval. Multi Systems 7, 467–476 (1999)

    Article  Google Scholar 

  12. Bai, X., Latecki, L.J., Liu, W.Y.: Skeleton pruning by contour partitioning with discrete curve evolution. IEEE Trans. Pattern Anal Mach. Intell. 29(30), 449–462 (2007)

    Article  Google Scholar 

  13. Khan, S., Sanches, J., Ventura, R.: Robust band profile extraction using constrained nonparametric machine-learning technique. IEEE Trans. Biomed. Eng. 57(10), 2587–2591 (2010)

    Article  Google Scholar 

  14. Mahmoodi, S., Sharif, B.S.: Contour evolution scheme for variation image segmentation and smoothing. IET J. Dig. Object Identif. 1(3), 287–294 (2007)

    Google Scholar 

  15. Lai, H.C., Savvides, M., Chen, T.: Proposed FPGA hardware architecture for high frame rate (≥100fps) face detection using feature cascade classifiers. In: 1st IEEE International Conference on Biometrics: Theory, Applications, and Systems, 27–29 Sept 2007, pp. 1–6 (2007)

  16. Yao, M., Yi, W., Zhu, R., Chen, R.: Semantic image retrieval based on multiple-instance learning. In: 10th IEEE International Conference on Data Mining Workshops (ICDMW), 13–13 Dec 2010, pp. 905–910 (2010)

  17. Wang, B., Zhang, X., Zhao, X.Y., Zhang, Z. D., Zhang, H.Z.: A semantic description for content-based image retrieval. In: International Conference on Machine Learning and Cybernetics, 12–15 July 2008, pp. 2466–2469 (2008)

  18. Akakin, H.C., Gurcan, M.N.: Content-based microscopic image retrieval system for multi-image queries. IEEE Trans Inf. Technol. Biomed. 16(4), 758–769 (2012)

    Article  Google Scholar 

  19. Liu, Y., Zhang, D., Lu, G., Ma, W.Y.: A survey of content-based image retrieval with high-level semantics. Pattern Recogn. 40, 262–282 (2007)

    Article  MATH  Google Scholar 

  20. Rahman, M.M., Desai, B.C., Bhattacharya, P.: A feature level fusion in similarity matching to content-based image retrieval. In: 9th International Conference on Information Fusion (Fusion), 10–13 July 2006, pp. 1–6 (2006)

  21. Mostefaoui, S.K., Maamar, Z., Giaglis, G.M.: Advanced in ubiquitous computing: future paradigms and directions. IGI Publishing, Hershey (2008)

    Book  Google Scholar 

  22. Remagnino, P., Foresti, G.L.: Ambient intelligence: a new multidisciplinary paradigm. IEEE Trans. Syst. Man Cybern. Part A Syst. Hum. 35(1), 1–6 (2005)

    Article  Google Scholar 

  23. Kim, T., Choi, Y., Ham, S., Chung, J.Y., Hyun, J., Li, J., Hong, J. W-K.: Monitoring and detecting abnormal behavior in mobile cloud infrastructure. In: IEEE Network Operations and Management Symposium. pp. 1303–1310 (2012)

  24. Yu, Y.H., Kwok, N., Ha, Q.P.: Color tracking for multiple robot control using a system-on-programmable-chip. Autom Constr. 20, 669–676 (2011)

    Article  Google Scholar 

  25. Tsai, Y.K., Jian, L.Y., Hsu, P.L., Wang, B.C.: Implementation of autonomous vehicles with the hough transform and fuzzy control. In: SICE Annual Conference, pp. 2095–2101 (2007)

  26. Halder, S., Bhattacharjee, D., Nasipuri, M., Basu, D. K.: A fast FPGA based architecture for sobel edge detection. In: Progress in VLSI Design and Test, vol. 7373, pp. 300–306 (2012)

  27. Zhong, F., Capson, D.W., Schuurman, D.: Parallel architecture for PCA image feature detection using FPGA. In: International Conference on Electrical and Computer Engineering (CCECE), 4–7 May 2008, pp. 1341–1344 (2008)

  28. Bahoura, M., Ezzaidi, H.: FPGA implementation of a feature extraction technique based on fourier transform. In: 24th International Conference on Microelectronics (ICM), 16–20 Dec 2012, pp. 1–4 (2012)

  29. Zhou, X., Tomagou, N., Ito, Y., Nakano, K.: Efficient Hough transform on the FPGA using DSP slices and block RAMs. In: 27th International Symposium on Parallel and Distributed Processing Workshops and PhD Forum (IPDPSW), pp. 771–778 (2013)

  30. Yao, L., Feng, H., Zhu, Y., Jiang, Z., Zhao, D., Feng, W.: An architecture of optimised SIFT feature detection for an FPGA implementation of an image matcher. In: International Conference on Field-programmable Technology (FPT), 9–11 Dec 2009, pp. 30–37 (2009)

  31. Latecki, L.J., Lakämper, R.: Convexity rule for shape decomposition based on discrete contour evolution. Comput. Vis. Image Underst. 73(3), 441–454 (1999)

    Article  Google Scholar 

  32. Chowhan, S.S., Shinde, G.N.: Evaluation of statistical feature encoding techniques on iris images. In: 29th World Congress on Computer Science and Information Engineering, Mar 31–Apr 2 2009, pp. 71–75 (2009)

  33. Buciu, I., Gacsadi, A.: Spatiotemporal facial features encoding for facial expression analysis in image sequences. In: 10th International Symposium on Signals, Circuits and Systems (ISSCS), June 30–July 1 2011, pp. 1–4 (2011)

  34. Huang, C., Ai, H., Li, Y., Lao, S.: High-performance rotation invariant multiview face detection. IEEE Trans. Pattern Anal. Mach. Intell. 29(4), 671–686 (2007)

    Article  Google Scholar 

  35. Mörwald, T., Prankl, J.: Extensions for robust object tracking with a Monte Carlo particle filter. J. Real Time Image Process. p. 15 (online)

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Acknowledgments

Supports from Ministry of Science and Technology, funded by the government of Taiwan under Grant NSC 99-2221-E-027-057-MY3, Ministry of Education Taiwan for the Top University Project to the National Cheng Kung University (NCKU), and Mr. Yao-Long Cai are gratefully acknowledged.

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Author notes

  1. Tsu-Tian Lee

    Present address: Department of Electrical Engineering, Chung Yuan Christian University, 32023, 200 Chung Pei Rd, Chung Li City, Taiwan

Authors and Affiliations

  1. Department of Electrical Engineering, National Chung Cheng University, No. 168, University Rd., Min-Hsiung Township, Chiayi County, 62102, Taiwan

    Ying-Hao Yu

  2. Department of Electrical Engineering, Tamkang University, No. 151, Yingzhuan Rd, Tamsui Dist, New Taipei, 25137, Taiwan

    Tsu-Tian Lee

  3. Department of Computer Science and Information Engineering, National Cheng Kung University, No. 1, University Rd, Tainan, 701, Taiwan

    Pei-Yin Chen

  4. School of Mechanical and Manufacturing Engineering, The University of New South Wales, Sydney, NSW, 2052, Australia

    Ngaiming Kwok

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  1. Ying-Hao Yu
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  4. Ngaiming Kwok
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Correspondence to Ying-Hao Yu.

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Yu, YH., Lee, TT., Chen, PY. et al. On-chip real-time feature extraction using semantic annotations for object recognition. J Real-Time Image Proc 15, 249–264 (2018). https://doi.org/10.1007/s11554-014-0474-2

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  • DOI: https://doi.org/10.1007/s11554-014-0474-2

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