Pattern Analysis and Applications

, Volume 21, Issue 1, pp 1–18 | Cite as

An empirical evaluation of translational and rotational invariance of descriptors and the classification of flower dataset

Survey
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Abstract

Object recognition and identification is used in the development of automatic systems in various domains. Latest research indicates that the performance of such systems depend on the efficiency in feature extraction; robust feature description and optimized classification or matching. This paper presents an empirical evaluation of efficiency and robustness of various gradient and binary descriptors with respect to translation, rotation and scaling etc. The performance of each descriptor is evaluated against the parameters such as size of feature set in terms of number of keypoints, matching accuracy and execution time. The detailed experiments were conducted on 17 category Oxford flower dataset to evaluate the robustness of descriptors against various rotations, scaling and noise using precision and recall values. Experimental results shows that the PCA-SIFT and SURF gives less matching rate but faster as compared to SIFT due to reduction in dimension in PCA-SIFT and use of integral images in SURF. ORB gives the best classification and outperforms the other descriptors with less memory requirement and is compact in size.

Keywords

Robustness Invariance Feature descriptors Precision and recall Key-points SVM classifier 

Notes

Acknowledgements

Authors are highly acknowledging the efforts of anonymous reviewers and editor in-chief of the journal for reviewing the manuscript and responding in very less time. Authors also thanks all others who have helped in preparing the manuscript

References

  1. 1.
    Anamandra SH, Chandrasekaran V (2016) COLOR CHILD: a novel color image local descriptor for texture classification and segmentation. Pattern Anal Appl 19(3):821–837MathSciNetCrossRefGoogle Scholar
  2. 2.
    Roberts L (1960) Pattern recognition with an adaptive network. In: Proceedings of the institute of radio engineers, vol 48, no. 3, pp 398–398Google Scholar
  3. 3.
    Tippett JT, Borkowitz DA, Clapp LC, Koester CJ, Vanderburgh Jr A (1965) Optical and electro-optical information processing. MASSACHUSETTS INST OF TECH CAMBRIDGEGoogle Scholar
  4. 4.
    Jimenez AR, Ceres R, Pons JL (2000) A survey of computer vision methods for locating fruit on trees. Trans ASAE Am Soc Agric Eng 43(6):1911–1920CrossRefGoogle Scholar
  5. 5.
    Malamas EN, Petrakis EG, Zervakis M, Petit L, Legat JD (2003) A survey on industrial vision systems, applications and tools. Image Vis Comput 21(2):171–188CrossRefGoogle Scholar
  6. 6.
    Ejiri M (2007) Machine vision in early days: Japan’s pioneering contributions. In: Computer vision–ACCV 2007, pp 35–53Google Scholar
  7. 7.
    Kumar R, Chandra P, Hanmandlu M (2016) A robust fingerprint matching system using orientation features. J Inf Process Syst 12(1):83–99Google Scholar
  8. 8.
    Aravindan A, Anzar SM (2017) Robust partial fingerprint recognition using wavelet SIFT descriptors. Pattern Anal Appl. doi: 10.1007/s10044-017-0615-x Google Scholar
  9. 9.
    Canny J (1986) A computational approach to edge detection. IEEE Trans Pattern Anal Mach Intell 6:679–698CrossRefGoogle Scholar
  10. 10.
    Kumar R, Chandra P, Hanmandlu M (2014) Rotational invariant fingerprint matching using local directional descriptors. Int J Comput Intell Stud 3(4):292–319CrossRefGoogle Scholar
  11. 11.
    Kumar R, Hanmandlu M, Chandra P (2014) An empirical evaluation of rotation invariance of LDP feature for fingerprint matching using neural networks. Int J Comput Vis Robot 4(4):330–348CrossRefGoogle Scholar
  12. 12.
    Lowe DG (1999) Object recognition from local scale-invariant features. In: The proceedings of the seventh IEEE international conference on computer vision, 1999, vol 2, pp 1150–1157Google Scholar
  13. 13.
    Lowe DG (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vis 60(2):91–110CrossRefGoogle Scholar
  14. 14.
    Bay H, Tuytelaars T, Van Gool L (2006) Surf: speeded up robust features. In: Computer vision–ECCV 2006, pp 404–417Google Scholar
  15. 15.
    Ke Y, Sukthankar R (2004) PCA-SIFT: a more distinctive representation for local image descriptors. In: Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition (CVPR'04). IEEE Computer Society. Washington DC, USA, pp 506–513Google Scholar
  16. 16.
    Tola E, Lepetit V, Fua P (2010) Daisy: an efficient dense descriptor applied to wide-baseline stereo. IEEE Trans Pattern Anal Mach Intell 32(5):815–830CrossRefGoogle Scholar
  17. 17.
    Dalal N, Triggs B (2005) Histograms of oriented gradients for human detection. In: IEEE computer society conference on computer vision and pattern recognition, 2005. CVPR 2005, vol 1, pp 886–893Google Scholar
  18. 18.
    Calonder M, Lepetit V, Strecha C, Fua P (2010) Brief: Binary robust independent elementary features. In: Computer vision–ECCV 2010, pp 778–792Google Scholar
  19. 19.
    Leutenegger S, Chli M, Siegwart RY (2011) BRISK: binary robust invariant scalable keypoints. In: 2011 IEEE international conference on computer vision (ICCV), pp 2548–2555Google Scholar
  20. 20.
    Rublee E, Rabaud V, Konolige K, Bradski G (2011) ORB: an efficient alternative to SIFT or SURF. In: 2011 IEEE international conference on computer vision (ICCV), pp 2564–2571Google Scholar
  21. 21.
    Alahi A, Ortiz R, Vandergheynst P (2012) Freak: fast retina keypoint. In: 2012 IEEE conference on computer vision and pattern recognition (CVPR), pp 510–517Google Scholar
  22. 22.
    Levi G, Hassner T (2016) LATCH: learned arrangements of three patch codes. In: 2016 IEEE winter conference on applications of computer vision (WACV), pp 1–9Google Scholar
  23. 23.
    Mikolajczyk K, Schmid C (2003) A performance evaluation of local descriptors. In: Proceedings of computer vision and pattern recognition, 2003 IEEE conference on, pp 257–264Google Scholar
  24. 24.
    Roshanbin N, Miller J (2016) A comparative study of the performance of local feature-based pattern recognition algorithms. Pattern Anal Appl. doi: 10.1007/s10044-016-0554-y Google Scholar
  25. 25.
    Mikolajczyk K, Schmid C (2005) A performance evaluation of local descriptors. IEEE Trans Pattern Anal Mach Intell 27(10):1615–1630CrossRefGoogle Scholar
  26. 26.
    Tuytelaars T, Mikolajczyk K (2008) Local invariant feature detectors: a survey. Found Trends® Comput Graph Vis 3(3):177–280CrossRefGoogle Scholar
  27. 27.
    Rani R, Kumar R, Singh AP (2016) An empirical evaluation of local descriptors in object recognition. In: 2016 international conference on advances in computing, communications and informatics (ICACCI), pp 1517–1521Google Scholar
  28. 28.
    Gauglitz S, Höllerer T, Turk M (2011) Evaluation of interest point detectors and feature descriptors for visual tracking. Int J Comput Vis 94(3):335–360CrossRefMATHGoogle Scholar
  29. 29.
    Harris C, Stephens M (1988) A combined corner and edge detector. In: Alvey vision conference, vol 15, no. 50, pp 10-5244Google Scholar
  30. 30.
    Rosten E, Drummond T (2006) Machine learning for high-speed corner detection. In: Computer vision–ECCV 2006, pp 430–443Google Scholar
  31. 31.
    Lindeberg T (1998) Feature detection with automatic scale selection. Int J Comput Vis 30(2):79–116CrossRefGoogle Scholar
  32. 32.
    Li Z, Liu J, Yang Y, Zhou X, Lu H (2014) Clustering-guided sparse structural learning for unsupervised feature selection. IEEE Trans Knowl Data Eng 26(9):2138–2150CrossRefGoogle Scholar
  33. 33.
    Li Z, Tang J (2015) Unsupervised feature selection via nonnegative spectral analysis and redundancy control. IEEE Trans Image Process 24(12):5343–5355MathSciNetCrossRefGoogle Scholar
  34. 34.
    Li Z, Liu J, Tang J, Lu H (2015) Robust structured subspace learning for data representation. IEEE Trans Pattern Anal Mach Intell 37(10):2085–2098CrossRefGoogle Scholar
  35. 35.
    Abdel-Hakim AE, Farag AA (2006) CSIFT: A SIFT descriptor with color invariant characteristics. In: 2006 IEEE computer society conference on computer vision and pattern recognition, vol 2, pp 1978–1983Google Scholar
  36. 36.
    Mainali P, Lafruit G, Yang Q, Geelen B, Van Gool L, Lauwereins R (2013) SIFER: scale-invariant feature detector with error resilience. Int J Comput Vis 104(2):172–197CrossRefMATHGoogle Scholar
  37. 37.
    Mortensen EN, Deng H, Shapiro L (2005) A SIFT descriptor with global context. In: IEEE computer society conference on computer vision and pattern recognition, 2005. CVPR 2005, vol 1, pp 184–190Google Scholar
  38. 38.
    Morel JM, Yu G (2009) ASIFT: a new framework for fully affine invariant image comparison. SIAM J Imaging Sci 2(2):438–469MathSciNetCrossRefMATHGoogle Scholar
  39. 39.
    Mikolajczyk K, Schmid C (2004) Scale and affine invariant interest point detectors. Int J Comput Vis 60(1):63–86CrossRefGoogle Scholar
  40. 40.
    Alcantarilla PF, Bergasa LM, Davison AJ (2013) Gauge-SURF descriptors. Image Vis Comput 31(1):103–116CrossRefGoogle Scholar
  41. 41.
    Strecha C, Bronstein A, Bronstein M, Fua P (2012) LDAHash: improved matching with smaller descriptors. IEEE Trans Pattern Anal Mach Intell 34(1):66–78CrossRefGoogle Scholar
  42. 42.
    Trzcinski T, Christoudias M, Fua P, Lepetit V (2013) Boosting binary keypoint descriptors. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2874–2881Google Scholar
  43. 43.
    Nilsback ME, Zisserman A (2006) A visual vocabulary for flower classification. In: 2006 IEEE computer society conference on computer vision and pattern recognition, vol 2. IEEE, pp 1447–1454Google Scholar
  44. 44.
    Heath MD, Sarkar S, Sanocki T, Bowyer KW (1997) A robust visual method for assessing the relative performance of edge-detection algorithms. IEEE Trans Pattern Anal Mach Intell 19(12):1338–1359CrossRefGoogle Scholar
  45. 45.
    Wirth MA (2005) Performance evaluation of image processing algorithms in CADe. Technol Cancer Res Treat 4(2):159–172CrossRefGoogle Scholar
  46. 46.
    Kumar R, Chandra P, Hanmandlu M (2014) Rotational invariant fingerprint matching using local directional descriptors. Int J Comput Intell Stud 3(4):292–319CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London Ltd. 2017

Authors and Affiliations

  1. 1.USICTGGSIPUDelhiIndia
  2. 2.HMRITMAffiliated with GGSIPUDelhiIndia

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