Abstract
In this paper, a new local feature descriptor called GPCA-SIFT is proposed for scene image classification. Like PCA-SIFT, we get the key points using the detection method in Scale Invariant Feature Transform (SIFT) and extract a 41 * 41 patch for each key point. Then we calculate the horizontal and vertical gradient of each pixel in the patch. However, instead of concatenating two gradient matrices, we directly work with the two-dimensional matrix and apply Generalized Principal Component Analysis (GPCA) to reduce it to a lower-dimensional matrix. Finally, we concatenate the reduced matrix and form a 1D vector. Compared with Principal Component Analysis (PCA), it preserves more spatial locality information. When applied in multi-class scene image classification, our proposed descriptor outperforms other related algorithms in terms of classification accuracy.
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
Mikolajczyk, K., Schmid, C.: A performance evaluation of local descriptors. IEEE Trans. Pattern Anal. Mach. Intell. 27(10), 1615–1630 (2005)
Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vision 60(2), 91–110 (2004)
Bay, H., Tuytelaars, T., Gool, L.: SURF: Speeded Up Robust Features. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3951, pp. 404–417. Springer, Heidelberg (2006). doi:10.1007/11744023_32
Rosten, E., Drummond, T.: Machine learning for high-speed corner detection. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3951, pp. 430–443. Springer, Heidelberg (2006). doi:10.1007/11744023_34
Calonder, M., Lepetit, V., Strecha, C., Fua, P.: BRIEF: Binary Robust Independent Elementary Features. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010. LNCS, vol. 6314, pp. 778–792. Springer, Heidelberg (2010). doi:10.1007/978-3-642-15561-1_56
Rublee, E., Rabaud, V., Konolige, K.: ORB: an efficient alternative to SIFT or SURF. In: 2011 IEEE International Conference on Computer Vision (ICCV), pp. 2564–2571. IEEE (2011)
Leutenegger, S., Chli, M., Siegwart, R.: Binary robust invariant scalable keypoints. In: 2011 IEEE International Conference on Computer Vision (ICCV), pp. 2548–2555. IEEE (2011)
Alahi, A., Ortiz, R., Vandergheynst, P.: Fast retina keypoint. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 510–517. IEEE (2012)
Ke, Y., Sukthankar, R.-S.: 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, pp. II-506–II-513. IEEE (2004)
Yang, J., Zhang, D., Frangi, A.F., et al.: Two-dimensional PCA: a new approach to appearance-based face representation and recognition. IEEE Trans. Pattern Anal. Mach. Intell. 26(1), 131–137 (2004)
Li, M., Yuan, B.: 2D-LDA: a statistical linear discriminant analysis for image matrix. Pattern Recogn. Lett. 26(5), 527–532 (2005)
Yang, J., Xu, Y., Yang, J.Y.: Bi-2DPCA: a fast face coding method for recognition. In: Pattern Recognition Recent Advances, pp. 313–340 (2010)
Yan, X.-J., Zhao, C.-X., Yuan, X.: 2DPCA-SIFT: an efficient local feature descriptor. Acta Automatica Sinica 40(4), 675–682 (2014)
Ye, J., Janardan, R., Li, Q.: GPCA: an efficient dimension reduction scheme for image compression and retrieval. In: Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 354–363. ACM (2004)
Hartigan, J., Wang, M.: A k-means clustering algorithm. Appl. Stat. 28, 100–108 (1979)
Fei-Fei, L., Perona, P.: A Bayesian hierarchical model for learning natural scene categories. In: IEEE Computer Vision, Pattern Recognition, pp. 524–531. IEEE, New York (2005)
Barla, A., Odone, F., Verri, A.: Histogram intersection kernel for image classification. Int. Conf. Image Process. 3(2), III-513–III-516 (2003)
Chang, C.-C., Lin, C.-J.: LIBSVM: a library for support vector machines [EB/OL] (2001). http://www.csie.ntu.edu.tw/cjlin/libsvm
Oliva, A., Torralba, A.: Modeling the shape of the scene: a holistic representation of the spatial envelope. Int. J. Comput. Vision 42(3), 145–175 (2001)
Lazebnik, S., Schmid, C., Ponce, J.: Beyond bags of features: spatial pyramid classification for recognizing natural scene categories. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, New York, pp. 2169–2178 (2006)
Acknowledgment
This project is partly supported by NSF of China (61375001, 31200747), the Natural Science Foundation of Jiangsu Province (No.BK20140638, BK2012437), the Fundamental Research Funds for the Central Universities.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Ju, L., Xie, K., Zheng, H., Zhang, B., Yang, W. (2016). GPCA-SIFT: A New Local Feature Descriptor for Scene Image Classification. In: Tan, T., Li, X., Chen, X., Zhou, J., Yang, J., Cheng, H. (eds) Pattern Recognition. CCPR 2016. Communications in Computer and Information Science, vol 663. Springer, Singapore. https://doi.org/10.1007/978-981-10-3005-5_24
Download citation
DOI: https://doi.org/10.1007/978-981-10-3005-5_24
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-3004-8
Online ISBN: 978-981-10-3005-5
eBook Packages: Computer ScienceComputer Science (R0)