Abstract
Image classification involves extraction of repeatable and robust feature points from images for classification. These extracted feature points are described in terms of feature vectors. Scale Invariant Feature Transform (SIFT) is one of the popular feature descriptor for obtaining the feature vectors from images for image classification, image retrieval and object recognition. However, the dimension of feature vector obtained using SIFT is high. Hence, the computation time to build decision model using SIFT for image classification, image retrieval and object recognition is high and also requires large memory. In this work, we have proposed SIFT-64 and SIFT-32 to reduce the dimension of the conventional SIFT for image classification. To check the efficacy of the proposed SIFT-64 and SIFT-32, experiments were performed on two well-known publicly available datasets namely, CalTech6 and Graz. The performance is evaluated in terms of classification accuracy, training and testing time. Experimental results demonstrate superior performance of the SIFT-64 and SIFT-32 in comparison to conventional SIFT in terms of both training and testing time without compromising much on classification accuracy. The proposed feature descriptor outperforms the PCA-SIFT descriptor in terms of classification accuracy, training and testing time.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Bhatia, A.: Hessian-Laplace feature detector and Haar descriptor for image matching. Ph.D. thesis, University of Ottawa, Canada, May 2007
Ramisa, A., Vasudevan, S., Aldavert, D., Toledo, R., Lopez de Mantaras, R.: Evaluation of the SIFT object recognition method in mobile robots (2009)
Liu, J., Meng, F., Mu, F., Zhang, Y.: An improved image retrieval method based on SIFT algorithm and saliency map. In: 2014 11th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD), pp. 766–770. IEEE (2014)
Sargent, D., Chen, C.I., Tsai, C.M., Wang, Y.F., Koppel, D.: Feature detector and descriptor for medical images. In: SPIE Medical Imaging, p. 72592Z. International Society for Optics and Photonics (2009)
Ghassabi, Z., Shanbehzadeh, J., Sedaghat, A., Fatemizadeh, E.: An efficient approach for robust multimodal retinal image registration based on UR-SIFT features and PIIFD descriptors. EURASIP J. Image Video Process. 2013(1), 1–16 (2013)
Amerini, I., Barni, M., Caldelli, R., Costanzo, A.: Removal and injection of keypoints for SIFT-based copy-move counter-forensics. EURASIP J. Inf. Secur. 2013(1), 1 (2013)
Dellinger, F., Delon, J., Gousseau, Y., Michel, J., Tupin, F.: SAR-SIFT: a SIFT-like algorithm for applications on SAR images. In: 2012 IEEE International Geoscience and Remote Sensing Symposium, pp. 3478–3481. IEEE (2012)
Petry, M.R., Moreira, A.P., Reisinst, L.P.: Increasing illumination invariance of SURF feature detector through color constancy. In: Correia, L., Reis, L.P., Cascalho, J. (eds.) Portuguese Conference on Artificial Intelligence, pp. 259–270. Springer, Heidelberg (2013)
Bravo, C., Sánchez, N., GarcÃa, N., Menéndez, J.M.: Outdoor vacant parking space detector for improving mobility in smart cities. In: Correia, L., Reis, L.P., Cascalho, J. (eds.) Portuguese Conference on Artificial Intelligence, pp. 30–41. Springer, Heidelberg (2013)
Andreopoulos, A., Tsotsos, J.K.: 50 years of object recognition: directions forward. Comput. Vis. Image Underst. 117(8), 827–891 (2013)
Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vision 60(2), 91–110 (2004)
Ke, Y., Sukthankar, R.: 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 2004, vol. 2, p. II-506. IEEE (2004)
Mikolajczyk, K., Schmid, C.: A performance evaluation of local descriptors. IEEE Trans. Pattern Anal. Mach. Intell. 27(10), 1615–1630 (2005)
Juan, L., Gwun, O.: A comparison of SIFT, PCA-SIFT and SURF. Int. J. Image Process. (IJIP) 3(4), 143–152 (2009)
Khan, N.Y., McCane, B., Wyvill, G.: SIFT and SURF performance evaluation against various image deformations on benchmark dataset. In: 2011 International Conference on Digital Image Computing Techniques and Applications (DICTA), pp. 501–506. IEEE (2011)
Zhang, J., Marszałek, M., Lazebnik, S., Schmid, C.: Local features and kernels for classification of texture and object categories: a comprehensive study. Int. J. Comput. Vision 73(2), 213–238 (2007)
Rubner, Y., Tomasi, C., Guibas, L.J.: A metric for distributions with applications to image databases. In: Sixth International Conference on Computer Vision, 1998, pp. 59–66. IEEE (1998)
Fergus, R., Perona, P., Zisserman, A.: Object class recognition by unsupervised scale-invariant learning. In: 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings, vol. 2, p. II-264. IEEE (2003)
Opelt, A., Fussenegger, M., Pinz, A., Auer, P.: Weak hypotheses and boosting for generic object detection and recognition. In: Pajdla, T., Matas, J. (eds.) Computer Vision-ECCV 2004, pp. 71–84. Springer, Heidelberg (2004)
Acknowledgments
The authors express their gratitude to the Council of Scientific & Industrial Research (CSIR), India, for the obtained financial support in performing this research work.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG
About this paper
Cite this paper
Kumar, D., Agrawal, R.K. (2018). Dimensionality Reduction of SIFT Descriptor Using Vector Decomposition for Image Classification. In: Abraham, A., Cherukuri, A., Madureira, A., Muda, A. (eds) Proceedings of the Eighth International Conference on Soft Computing and Pattern Recognition (SoCPaR 2016). SoCPaR 2016. Advances in Intelligent Systems and Computing, vol 614. Springer, Cham. https://doi.org/10.1007/978-3-319-60618-7_10
Download citation
DOI: https://doi.org/10.1007/978-3-319-60618-7_10
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-60617-0
Online ISBN: 978-3-319-60618-7
eBook Packages: EngineeringEngineering (R0)