Abstract
Object recognition has a wide domain of applications such as content-based image classification, video data mining, video surveillance and more. Object recognition accuracy has been a significant concern. Although deep learning had automated the feature extraction but hand crafted features continue to deliver consistent performance. This paper aims at efficient object recognition using hand crafted features based on Oriented Fast & Rotated BRIEF (Binary Robust Independent Elementary Features) and Scale Invariant Feature Transform features. Scale Invariant Feature Transform (SIFT) are particularly useful for analysis of images in light of different orientation and scale. Locality Preserving Projection (LPP) dimensionality reduction algorithm is explored to reduce the dimensions of obtained image feature vector. The execution of the proposed work is tested by using k-NN, decision tree and random forest classifiers. A dataset of 8000 samples of 100-class objects has been considered for experimental work. A precision rate of 69.8% and 76.9% has been achieved using ORB and SIFT feature descriptors, respectively. A combination of ORB and SIFT feature descriptors is also considered for experimental work. The integrated technique achieved an improved precision rate of 85.6% for the same.
Similar content being viewed by others
References
Alhassan AK, Alfaki AA (2017) Color and Texture Fusion-Based Method for Content-Based Image Retrieval. Proceedings of the International Conference on Communication, Control, Computing and Electronics Engineering, 1–6
Ballard DH (1981) Generalizing the Hough transform to detect arbitrary shapes. Pattern Recogn 13(2):111–122
Belongie S, Malik J, Puzicha J (2002) Shape matching and object recognition using shape contexts. IEEE Trans Pattern Anal Mach Intell 24(4):509–522
Berg AC, Berg TL, Malik J (2005) Shape matching and object recognition using low distortion correspondences. In Proceedings of Computer Vision and Pattern Recognition, 1:26–33
Breiman L (2001) Random forests. Mach Learn 45(1):5–32
Cestnik B, Kononenko I, Bratko I (1987) Assistant 86: A knowledge elicitation tool for sophisticated users. Proceedings of the 2nd European Working Session on Learning, Bled, Yugoslavia, 31–45
Cover T, Hart P (1967) Nearest neighbor pattern classification. IEEE Trans Inf Theory 13(1):21–27
Davis J, Goadrich M (2006) The relationship between Precision-Recall and ROC curves. Proceedings of the 23rd International Conference on Machine learning, Pennsylvania, USA, 233–240
Elnagara A, Alhajj R (2003) Segmentation of connected handwritten numeral strings. Pattern Recogn 36(3):625–634
Fei-Fei L, Fergus R, Perona P (2004) Learning generative visual models from few training examples: an incremental Bayesian approach tested on 101 object categories. In proceedings of the Workshop on Generative-Model Based Vision. Washington, DC, June 2004
Ferrari V, Jurie F, Schmid C (2010) From Images to Shape Models for Object Detection. Int J Comput Vis 87(3):284–303
Grauman K, Darrell T (2005) The pyramid match kernel: Discriminative classification with sets of image features. Proceedings of the 10th IEEE International Conference on Computer Vision, 2:1458–1465
He X, Niyogi P (2004) Locality preserving projections. Advances in Neural Information Processing systems, 153–160
Huang X, Xu Y, Yang L (2017) Local visual similarity descriptor for describing local region. Proceedings of theNinth International Conference on Machine Vision (ICMV 2016), 10341: 103410S
Kanungo T, Mount DM, Netanyahu NS, Piatko CD, Silverman R, Wu AY (2002) An efficient k-means clustering algorithm: Analysis and implementation. IEEE Trans Pattern Anal Mach Intell 24(7):881–892
Lazebnik S, Schmid C, Ponce J (2006) Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2:2169–2178
Leordeanu M, Hebert M, Sukthankar R (2007) Beyond local appearance: Category recognition from pairwise interactions of simple features. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1–8
Liu X, Zhang R, Meng Z, Hong R, Liu G (2019) On fusing the latent deep CNN feature for image classification. World Wide Web 22(2):423–436
Lowe DG (2004) Distinctive image features from scale invariant key points. Int J Comput Vis 60:91–110
Minut S, Mahadevan S (2001) A reinforcement learning model of selective visual attention. Proceedings of the fifth international conference on Autonomous Agents, 457–464
Montazer GA, Giveki D (2015) Content based image retrieval system using clustered scale invariant feature transforms. Optik-International Journal for Light and Electron Optics 126(18):1695–1699
Mori G, Belongie S, Malik J (2005) Efficient shape matching using shape contexts. IEEE Trans Pattern Anal Mach Intell 27(11):1832–1837
Nadernejad E, Sharifzadeh S, Hassanpour H (2008) Edge Detection Techniques: Evaluation and Comparisons. Appl Math Sci 2(31):1507–1520
Peizhong L, Guo J, Chamnongthai K, Prasetyo H (2017) Fusion of Color Histogram and LBP-based Features for Texture Image Retrieval and Classification. Inf Sci 390:95–111
Peterson MA, Gibson BS (1994) Must Fig.-Ground Organization Precede Object Recognition? An Assumption in Peril. Psychol Sci 5(5):253–259
Sharma KU, Thakur NV (2017) A review and an approach for object detection in images. International Journal of Computational Vision and Robotics 7(1/2):196–237
Soltanshahi MA, Montazer GA, Giveki D (2015) Content Based Image Retrieval System Using Clustered Scale Invariant Feature Transforms. Optik - International Journal for Light and Electron Optics 126(18):1695–1699
Swain PH, Hauska H (1977) The decision tree classifier: Design and potential. IEEE Trans Geosci Electron 15(3):142–147
Toshev A, Taskar B, Daniilidis K (2010) Object detection via boundary structure segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 950–957
Ullman S (1984) Visual Routines. Cognition 18:97–157
Vinay A, Kumar CA, Shenoy GR, Murthy KB, Natarajan S (2015) ORB-PCA based feature extractiontechnique for face recognition. Proc Comput Sci 58:614–621
Yu SU, Shi J (2003) Object-specific Fig.-ground segregation. Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), 2:39–45
Zhang J, Marszałek M, Lazebnik S, Schmid C (2007) Local features and kernels for classification of texture and object categories: A comprehensive study. Int J Comput Vis 73(2):213–238
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Gupta, S., Kumar, M. & Garg, A. Improved object recognition results using SIFT and ORB feature detector. Multimed Tools Appl 78, 34157–34171 (2019). https://doi.org/10.1007/s11042-019-08232-6
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11042-019-08232-6