Skip to main content

Detection and Description of Image Features: An Introduction

  • Chapter
  • First Online:

Part of the book series: Studies in Computational Intelligence ((SCI,volume 630))

Abstract

Detection and description of image features play a vital role in various application domains such as image processing, computer vision, pattern recognition, and machine learning. There are two type of features that can be extracted from an image content; namely global and local features. Global features describe the image as a whole and can be interpreted as a particular property of the image involving all pixels; while, the local features aim to detect keypoints within the image and describe regions around these keypoints. After extracting the features and their descriptors from images, matching of common structures between images (i.e., features matching) is the next step for these applications. This chapter presents a general and brief introduction to topics of feature extraction for a variety of application domains. Its main aim is to provide short descriptions of the chapters included in this book volume.

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

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   109.99
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

Learn about institutional subscriptions

References

  1. Klette, R.: Concise Computer Vision: An introduction into Theory and Algorithms. Springer, USA (2014)

    Book  Google Scholar 

  2. Raxle, C.-C.: Automatic vehicle detection using local features-a statistical approach. IEEE Trans. Intell. Transp. Syst. 9(1), 83–96 (2008)

    Article  Google Scholar 

  3. Mukhtar, A., Likun, X.: Vehicle detection techniques for collision avoidance systems: A review. IEEE Trans. Intell. Transp. Syst. 16(5), 2318–2338 (2015)

    Article  Google Scholar 

  4. Geronimo, D., Lopez, A., Sappa, A., Graf, T.: Survey of pedestrian detection for advanced driver assistance systems. IEEE Trans. Pattern Anal. Mach. Intell. 32(7), 1239–1258 (2010)

    Article  Google Scholar 

  5. Da-Wen, S.: Computer Vision Technology for Food Quality Evaluation. Academic Press, Elsevier (2008)

    Google Scholar 

  6. Menze, B.H., Langs, G., Lu, L., Montillo, A., Tu, Z., Criminisi, A.: Medical computer vision: recognition techniques and applications in medical imaging. LNCS 7766 (2013)

    Google Scholar 

  7. Koen, E., Gevers, T., Snoek, G.: Evaluating color descriptors for object and scene recognition. IEEE Trans. Pattern Anal. Mach. Intell. 32(9), 1582–1596 (2010)

    Article  Google Scholar 

  8. Szeliski, R.: Computer Vision: Algorithms and Applications. Springer, USA (2011)

    Book  Google Scholar 

  9. Chen, Z., Sun, S.K.: A Zernike moment phase-based descriptor for local image representation and matching. IEEE Trans. Image Process. 19(1), 205–219 (2010)

    Article  MathSciNet  Google Scholar 

  10. Andreopoulos, A., Tsotsos, J.: 50 years of object recognition: directions forward. Comput. Vis. Image Underst. 117(8), 827–891 (2013)

    Article  Google Scholar 

  11. Mikolajczyk, K., Tuytelaars, T., Schmid, C., Zisserman, A., Matas, J., Schaffalitzky, F., Kadir, T., Gool, L.: A comparison of affine region detectors. Int. J. Comput. Vis. 65(1/2), 43–72 (2005)

    Article  Google Scholar 

  12. Moreels, P., Perona, P.: Evaluation of features detectors and descriptors based on 3D objects. Int. J. Comput. Vis. 73(3), 263–284 (2007)

    Article  Google Scholar 

  13. Gonzalez, R.C., Woods, R.E.: Digital Image Processing, 3rd edn. Prentice-Hall Inc, USA (2007)

    Google Scholar 

  14. John, C.R.: The Image Processing Handbook, 6 edn. CRC Press, Taylor & Francis Group, USA (2011)

    Google Scholar 

  15. Li, J., Allinson, N.: A comprehensive review of current local features for computer vision. Neurocomputing 71(10–12), 1771–1787 (2008)

    Article  Google Scholar 

  16. Liu, S., Bai, X.: Discriminative features for image classification and retrieval. Pattern Recogn. Lett. 33(6), 744–751 (2012)

    Article  Google Scholar 

  17. Tuytelaars, T., Mikolajczyk, K.: Local invariant feature detectors: a survey. Found. Trends Comput. Graph. Vis. 3(3), 177–280 (2007)

    Article  Google Scholar 

  18. Mainali, P., Lafruit, G., Yang, Q., Geelen, B., Gool, L.V., Lauwereins, R.: SIFER: Scale-invariant feature detector with error resilience. Int. J. Comput. Vis. 104(2), 172–197 (2013)

    Article  MATH  Google Scholar 

  19. Zhang, Y., Tian, T., Tian, J., Gong, J., Ming, D.: A novel biologically inspired local feature descriptor. Biol. Cybern. 108(3), 275–290 (2014)

    Article  Google Scholar 

  20. Hugo, P.: Performance evaluation of keypoint detection and matching techniques on grayscale data. SIViP 9(5), 1009–1019 (2015)

    Article  Google Scholar 

  21. Bouchiha, R., Besbes, K.: Comparison of local descriptors for automatic remote sensing image registration. SIViP 9(2), 463–469 (2015)

    Article  Google Scholar 

  22. Bianco, S., Mazzini, D., Pau, D., Schettini, R.: Local detectors and compact descriptors for visual search: a quantitative comparison. Digital Sig. Process 44, 1–13 (2015)

    Article  Google Scholar 

  23. Takacs, G., Chandrasekhar, V., Tsai, S., Chen, D., Grzeszczuk, R., Girod, B.: Rotation-invariant fast features for large-scale recognition and real-time tracking. Sig. Process: Image Commun. 28(4), 334–344 (2013)

    Google Scholar 

  24. Seidenari, L., Serra, G., Bagdanov, A., Del Bimbo, A.: Local pyramidal descriptors for image recognition. IEEE Trans. Pattern Anal. Mach. Intell. 36(5), 1033–1040 (2014)

    Article  Google Scholar 

  25. Morevec, H.P.: Towards automatic visual obstacle avoidance. In: Proceedings of the 5th International Joint Conference on Artificial Intelligence, vol. 2, pp. 584–584. IJCAI’77. Morgan Kaufmann Publishers Inc., San Francisco (1977)

    Google Scholar 

  26. Harris, C., Stephens, M.: A combined corner and edge detection. In: Proceedings of the Fourth Alvey Vision Conference, pp. 147–151 (1988)

    Google Scholar 

  27. Smith, S., Brady, J.: Susan-a new approach to low level image processing. Int. J. Comput. Vis. 23(1), 45–78 (1997)

    Article  Google Scholar 

  28. Rosten, E., Drummond, T.: Fusing points and lines for high performance tracking. In: Proceedings of the Tenth IEEE International Conference on Computer Vision, vol. 2, pp. 1508–1515. ICCV’05, IEEE Computer Society, Washington, DC (2005)

    Google Scholar 

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

    Article  Google Scholar 

  30. Bay, H., Ess, A., Tuytelaars, T., Gool, L.V.: Speeded-up robust features (SURF). Comput. Vis. Image Underst. 110(3), 346–359 (2008)

    Article  Google Scholar 

  31. Schmid, C., Mohr, R., Bauckhage, C.: Evaluation of interest point detectors. Int. J. Comput. Vis. 37(2), 151–172 (2000)

    Article  MATH  Google Scholar 

  32. Possa, P., Mahmoudi, S., Harb, N., Valderrama, C., Manneback, P.: A multi-resolution FPGA-Based architecture for real-time edge and corner detection. IEEE Trans. Comput. 63(10), 2376–2388 (2014)

    Article  MathSciNet  Google Scholar 

  33. Jain, A.K., Ross, A.A., Nandakumar, K.: Introduction to Biometrics, 1st edn.Springer (2011)

    Google Scholar 

  34. Egawa, S., Awad, A.I., Baba, K.: Evaluation of acceleration algorithm for biometric identification. In: Benlamri, R. (ed.) Networked Digital Technologies, Communications in Computer and Information Science, vol. 294, pp. 231–242. Springer, Heidelberg (2012)

    Google Scholar 

  35. Awad, A.I.: Fingerprint local invariant feature extraction on GPU with CUDA. Informatica (Slovenia) 37(3), 279–284 (2013)

    Google Scholar 

  36. Awad, A.I.: Fast fingerprint orientation field estimation incorporating general purpose GPU. In: Balas, V.E., Jain, L.C., Kovaevi, B. (eds.) Soft Computing Applications, Advances in Intelligent Systems and Computing, vol. 357, pp. 891–902. Springer International Publishing (2016)

    Google Scholar 

  37. Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2001), vol. 1, pp. 511–518 (2001)

    Google Scholar 

  38. Awad, A.I., Baba, K.: Evaluation of a fingerprint identification algorithm with SIFT features. In: Proceedings of the 3rd 2012 IIAI International Conference on Advanced Applied Informatics, pp. 129–132. IEEE, Fukuoka, Japan (2012)

    Google Scholar 

  39. Leutenegger, S., Chli, M., Siegwart, R.: BRISK: Binary robust invariant scalable keypoints. In: IEEE International Conference on Computer Vision (ICCV), pp. 2548–2555 (2011)

    Google Scholar 

  40. Donoser, M., Bischof, H.: Efficient maximally stable extremal region (MSER) tracking. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recogn. 1, 553–560 (2006)

    Google Scholar 

  41. Shi, J., Tomasi, C.: Good features to track. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’94), pp. 593–600 (1994)

    Google Scholar 

  42. Calonder, M., Lepetit, V., Strecha, C., Fua, P.: BRIEF: Binary robust independent elementary features. In: Proceedings of the 11th European Conference on Computer Vision: Part IV, pp. 778–792. ECCV’10. Springer, Heidelberg (2010)

    Google Scholar 

  43. Rublee, E., Rabaud, V., Konolige, K., Bradski, G.: ORB: an efficient alternative to sift or surf. In: Proceedings of the 2011 International Conference on Computer Vision, pp. 2564–2571. ICCV ’11, IEEE Computer Society, Washington, DC (2011)

    Google Scholar 

  44. Pfau, R., Steinbach, M., Woll, B. (eds.): Sign Language: An International Handbook. Series of Handbooks of Linguistics and Communication Science (HSK), De Gruyter, Berlin (2012)

    Google Scholar 

  45. Trémeau, A., Tominaga, S., Plataniotis, K.N.: Color in image and video processing: Most recent trends and future research directions. J. Image Video Process. Color Image Video Process. 7:1–7:26 (2008)

    Google Scholar 

  46. Sezgin, M., Sankur, B.: Survey over image thresholding techniques and quantitative performance evaluation. J. Electron. Imaging 13(1), 146–168 (2004)

    Article  Google Scholar 

  47. Sun, Z., Tan, T.: Ordinal measures for iris recognition. IEEE Trans. Pattern Anal. Mach. Intell. 31(12), 2211–2226 (2009)

    Article  Google Scholar 

  48. Awad, A.I., Hassanien, A.E.: Impact of some biometric modalities on forensic science. In: Muda, A.K., Choo, Y.H., Abraham, A., N. Srihari, S. (eds.) Computational Intelligence in Digital Forensics: Forensic Investigation and Applications, Studies in Computational Intelligence, vol. 555, pp. 47–62. Springer International Publishing (2014)

    Google Scholar 

  49. Gutierrez, J., Epifanio, I., de Ves, E., Ferri, F.: An active contour model for the automatic detection of the fovea in fluorescein angiographies. In: Proceedings of the 15th International Conference on Pattern Recognition. vol. 4, pp. 312–315. (2000)

    Google Scholar 

  50. Fomenko, A., Kunii, T.: Topological Modeling for Visualization. Springer, Japan (2013)

    Google Scholar 

  51. Hero, A., Ma, B., Michel, O., Gorman, J.: Applications of entropic spanning graphs. IEEE Signal Process. Mag. 19(5), 85–95 (2002)

    Article  Google Scholar 

  52. Al-Shaher, A.A., Hancock, E.R.: Learning mixtures of point distribution models with the EM algorithm. Pattern Recogn. 36(12), 2805–2818 (2003)

    Article  MATH  Google Scholar 

  53. Peng, H., Long, F., Ding, C.: Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy. IEEE Trans. Pattern Anal. Mach. Intell. 27(8), 1226–1238 (2005)

    Article  Google Scholar 

  54. Lee, C.C., Chen, S.H.: Gabor wavelets and SVM classifier for liver diseases classiflcation from CT images. In: IEEE International Conference on Systems, Man and Cybernetics (SMC’06), vol. 1, pp. 548–552 (2006)

    Google Scholar 

  55. Christmann, A., Steinwart, I.: Support vector machines for classification. In: Support Vector Machines. Information Science and Statistics, pp. 285–329, Springer, New York (2008)

    Google Scholar 

  56. Lee, Y.J., Mangasarian, O.: SSVM: A smooth support vector machine for classification. Comput. Optim. Appl. 20(1), 5–22 (2001)

    Article  MathSciNet  MATH  Google Scholar 

  57. Cover, T., Hart, P.: Nearest neighbor pattern classification. IEEE Trans. Inf. Theory 13(1), 21–27 (2006)

    Article  Google Scholar 

  58. Wu, Y., Ianakiev, K., Govindaraju, V.: Improved k-nearest neighbor classification. Pattern Recogn. 35(10), 2311–2318 (2002)

    Article  MATH  Google Scholar 

  59. Specht, D.F.: Probabilistic neural networks. Neural Networks 3(1), 109–118 (1990)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding authors

Correspondence to M. Hassaballah or Ali Ismail Awad .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing Switzerland

About this chapter

Cite this chapter

Hassaballah, M., Awad, A.I. (2016). Detection and Description of Image Features: An Introduction. In: Awad, A., Hassaballah, M. (eds) Image Feature Detectors and Descriptors . Studies in Computational Intelligence, vol 630. Springer, Cham. https://doi.org/10.1007/978-3-319-28854-3_1

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-28854-3_1

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-28852-9

  • Online ISBN: 978-3-319-28854-3

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics