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2D object recognition: a comparative analysis of SIFT, SURF and ORB feature descriptors

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Abstract

Object recognition is a key research area in the field of image processing and computer vision, which recognizes the object in an image and provides a proper label. In the paper, three popular feature descriptor algorithms that are Scale Invariant Feature Transform (SIFT), Speeded Up Robust Feature (SURF) and Oriented Fast and Rotated BRIEF (ORB) are used for experimental work of an object recognition system. A comparison among these three descriptors is exhibited in the paper by determining them individually and with different combinations of these three methodologies. The amount of the features extracted using these feature extraction methods are further reduced using a feature selection (k-means clustering) and a dimensionality reduction method (Locality Preserving Projection). Various classifiers i.e. K-Nearest Neighbor, Naïve Bayes, Decision Tree, and Random Forest are used to classify objects based on their similarity. The focus of this article is to present a study of the performance comparison among these three feature extraction methods, particularly when their combination derives in recognizing the object more efficiently. In this paper, the authors have presented a comparative analysis view among various feature descriptors algorithms and classification models for 2D object recognition. The Caltech-101 public dataset is considered in this article for experimental work. The experiment reveals that a hybridization of SIFT, SURF and ORB method with Random Forest classification model accomplishes the best results as compared to other state-of-the-art work. The comparative analysis has been presented in terms of recognition accuracy, True Positive Rate (TPR), False Positive Rate (FPR), and Area Under Curve (AUC) parameters.

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References

  1. Abdelmajed AKA (2016) A comparative study of locality preserving projection and principle component analysis on classification performance using logistic regression. J Data Anal Inform Process 4(2):55–63

    Article  Google Scholar 

  2. Bansal M, Kumar M, Kumar M (2020a) 2D object recognition techniques: state-of-the-art work. Arch Computation Methods Eng. https://doi.org/10.1007/s11831-020-09409-1

  3. Bansal M, Kumar M, Kumar M (2020b). XGBoost: object recognition using shape descriptors and extreme gradient boosting classifier. DOI: https://doi.org/10.1007/978-981-15-6876-3_16.

  4. Bay H, Ess A, Tuytelaars T, Van GL (2008) Speeded-up robust features (SURF). Comput Vis Image Underst 110(3):346–359

    Article  Google Scholar 

  5. Bayraktar E, Boyraz P 2017 Analysis of feature detector and descriptor combinations with a localization experiment for various performance metrics. arXiv preprint arXiv:1710.06232

  6. Bosch A, Zisserman A, Munoz X 2007 Image classification using random forests and ferns. Proceedings of the IEEE 11th International Conference on Computer Vision (ICCV), 1-8.

  7. Calonder M, Lepetit V, Strecha C, Fua P 2010 BRIEF: binary robust independent elementary features. Proceedings of the European Conference on Computer Vision, 778-792.

  8. Chien HJ, Chuang CC, Chen CY, Klette R (2016) When to use what feature? SIFT, SURF, ORB, or A-KAZE features for monocular visual odometry. In: Proceedings of the International Conference on Image and Vision Computing. IVCNZ, New Zealand, pp 1–6

    Google Scholar 

  9. Gupta S, Kumar M, Garg A (2019) Improved Object Recognition Results using SIFT and ORB Feature Detector. Multimed Tools Appl 78:34157–34171

    Article  Google Scholar 

  10. Gupta S, Roy PP, Dogra DP, Kim BG (2020) Retrieval of colour and texture images using local directional peak valley binary pattern. Pattern Anal Applic 23:1569–1585. https://doi.org/10.1007/s10044-020-00879-4

    Article  Google Scholar 

  11. He X, Niyogi P (2004) Locality preserving projections. Adv Neural Inform Process Syst:153–160

  12. He K, Zhang X, Ren S and Sun J 2016 Deep residual learning for image recognition. In Proc IEEE Conf Comput Vis Pattern Recognition, 770-778.

  13. Jayech K. Mahjoub MA 2017 Object recognition based on dynamic random forests and SURF descriptor. Proceedings of the International Conference on Intelligent Data Engineering and Automated Learning, 355-364.

  14. Jeong D M, Kim J H, Lee Y W and Kim B G 2018 Robust weighted Keypoint matching algorithm for image retrieval. Proc Second Int Conf Video, Image Processing:145-149.

  15. Kabbai L, Abdellaoui M, Douik A (2019) Image classification by combining local and global features. Vis Comput 35(5):679–693

    Article  Google Scholar 

  16. Karami E, Prasad S and Shehata M 2017 Image matching using SIFT, SURF, BRIEF and ORB: performance comparison for distorted images. arXiv preprint arXiv:1710.02726

  17. Kim J, Kim BS, Savarese S 2012 Comparing image classification methods: K-nearest-neighbor and support-vector-machines. Appl Mathematics Electric Comput Eng 133-138.

  18. Kim B, Hong G, Psannis KE (2017) Design of efficient shape feature for object-based watermarking technology. Multimed Tools Appl 76:22741–22759. https://doi.org/10.1007/s11042-017-4344-3

    Article  Google Scholar 

  19. Kulkarni AV, Jagtap JS, Harpale VK (2013) Object recognition with ORB and its implementation on FPGA. Int J Adv Comput Res 3(3):164

    Google Scholar 

  20. Leutenegger S, Chli M, Siegwart R 2011 BRISK: binary robust invariant scalable Keypoints. Proceedings of the International Conference on Computer Vision (ICCV), 2548-2555.

  21. Loussaief S, Abdelkrim A 2018 Deep learning vs. bag of features in machine learning for image classification. Proc Int Conf Adv Syst Electric Technol, 6-10

  22. Lowe DG (2004) Distinctive image features from scale-invariant Keypoints. Int J Comput Vis 60(2):91–110

    Article  Google Scholar 

  23. Park DC (2016) Image classification using Naïve Bayes classifier. Int J Comput Sci Electronics Engineering (IJCSEE) 4(3):135–139

    Google Scholar 

  24. Patil MP, Kolhe SR (2014) Automatic image annotation using decision trees and rough sets. Int J Comput Sci Appl (IJCSA) 11(2):38–49

    Google Scholar 

  25. Rosten E and Drummond T 2006 Machine learning for high speed corner detection. Proc 9th Eur Conf Comput Vision, 1:430–443.

  26. Rublee E, Rabaut V, Konolige K, Bradski G 2011 ORB: an efficient alternative to SIFT or SURF, Proceedings of the IEEE International Conference on Computer Vision (ICCV), 1-8

  27. Schroff F, Criminisi A, Zisserman A 2008 Object class segmentation using random forests. Proc British Machine Vision Assoc Conference (BMVC), 1-10.

  28. Shermina J (2010) Application of locality preserving projections in face recognition. Int J Adv Comput Sci Appl 1(3):82–85

    Google Scholar 

  29. Shivakanth AM (2014) Object recognition using SIFT. Int J Innovat Sci Eng Technol (IJISET) 1(4):378–381

    Google Scholar 

  30. Simonyan K, Zisserman A 2014 Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556.

  31. Sivic J, Russell BC, Efros AA, Zisserman A, Freeman WT (2005) Discovering objects and their location in images. Proc Tenth IEEE Int Conf Computer Vision 1:370–377

    Article  Google Scholar 

  32. Srivastava D, Bakthula R, Agarwal S (2019) Image classification using SURF and bag of LBP features constructed by clustering with fixed centers. Multimed Tools Appl 78(11):14129–14153

    Article  Google Scholar 

  33. Tareen SAK, Saleem Z 2018 A comparative analysis of SIFT, SURF, KAZE, AKAZE, ORB, and BRISK. Proceedings of the International Conference on Computing, Mathematics and Engineering Technologies (iCoMET), 1-10

  34. Wang J, Yang J, Yu K, Lv F, Huang T, Gong Y 2010 Locality-constrained linear coding for image classification. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 3360-3367

  35. Yang F, XIE M (2017) Codebook learning for image recognition based on parallel key SIFT analysis. IEICE Trans Inf Syst 100(4):927–930

    Article  Google Scholar 

  36. Zhang H, Berg AC, Maire M, Malik J (2006) SVM-KNN: discriminative nearest neighbor classification for visual category recognition. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition 2:2126–2136

    Google Scholar 

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Authors and Affiliations

  1. Department of Computer Science, Punjabi University, Patiala, India

    Monika Bansal

  2. Department of Computational Sciences, Maharaja Ranjit Singh Punjab Technical University, Bathinda, Punjab, India

    Munish Kumar

  3. Department of Computer Science, Baba Farid College, Bathinda, Punjab, India

    Manish Kumar

Authors
  1. Monika Bansal
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  2. Munish Kumar
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  3. Manish Kumar
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Correspondence to Munish Kumar.

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The authors declare that they have no conflict of interest in this work. The authors have considered a public dataset, namely, Caltech-101 for experimental work.

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Bansal, M., Kumar, M. & Kumar, M. 2D object recognition: a comparative analysis of SIFT, SURF and ORB feature descriptors. Multimed Tools Appl 80, 18839–18857 (2021). https://doi.org/10.1007/s11042-021-10646-0

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