Abstract
Shape classification is an active research field due to its usefulness. In this work, hand crafted shape descriptors are combined with features extracted using convolutional neural network to do the classifi cation task. Extensive experiments were performed on public data sets to reveal the performance of the proposed method compared to the other state of the arts shape classification methods.
References
Atabay, H.A.: Binary shape classification using convolutional neural networks. IIOAB J. 7(5), 332–336 (2016)
Atabay, H.A.: A convolutional neural network with a new architecture applied on leaf classification. IIOAB J. 7(5), 226–331 (2016)
Bai, X., Liu, W., Tu, Z.: Integrating contour and skeleton for shape classification. In: 2009 IEEE 12th International Conference on Computer Vision Workshops (ICCV Workshops), pp. 360–367. IEEE (2009)
Beghin, T., Cope, J.S., Remagnino, P., Barman, S.: Shape and texture based plant leaf classification. In: Blanc-Talon, J., Bone, D., Philips, W., Popescu, D., Scheunders, P. (eds.) ACIVS 2010 Part II. LNCS, vol. 6475, pp. 345–353. Springer, Heidelberg (2010). doi:10.1007/978-3-642-17691-3_32
Belongie, S., Malik, J., Puzicha, J.: Shape matching and object recognition using shape contexts. IEEE Trans. Pattern Anal. Mach. Intell. 24(4), 509–522 (2002)
Daliri, M.R., Torre, V.: Robust symbolic representation for shape recognition and retrieval. Pattern Recogn. 41(5), 1782–1798 (2008)
Daliri, M.R., Torre, V.: Shape recognition based on kernel-edit distance. Comput. Vis. Image Underst. 114(10), 1097–1103 (2010)
Flusser, J., Suk, T.: Pattern recognition by affine moment invariants. Pattern Recogn. 26(1), 167–174 (1993)
Hu, D., Huang, W., Yang, J., Shang, L., Zhu, Z.: Shape matching and object recognition using common base triangle area. IET Comput. Vis. 9(5), 769–778 (2015)
Hu, M.K.: Visual pattern recognition by moment invariants. IRE Trans. Inf. Theory 8(2), 179–187 (1962)
Hu, R.X., Jia, W., Zhao, Y., Gui, J.: Perceptually motivated morphological strategies for shape retrieval. Pattern Recogn. 45(9), 3222–3230 (2012)
Kejia, W., Honggang, Z., Lunshao, C., Ping, Z., et al.: A comparative study of moment-based shape descriptors for product image retrieval. In: 2011 International Conference on Image Analysis and Signal Processing (IASP), pp. 355–359. IEEE (2011)
Latecki, L.J., Lakamper, R., Eckhardt, T.: Shape descriptors for non-rigid shapes with a single closed contour. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 424–429. IEEE (2000)
Leibe, B., Schiele, B.: Analyzing appearance and contour based methods for object categorization. In: Proceedings of 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, pp. II–409. IEEE (2003)
Lin, C., Pun, C.M.: Shape classification using hybrid regional and global descriptor. Int. J. Mach. Learn. Comput. 4(1), 68 (2014)
Ling, H., Jacobs, D.W.: Shape classification using the inner-distance. IEEE Trans. Pattern Anal. Mach. Intell. 29(2) 286–299 (2007)
Mallah, C., Cope, J., Orwell, J.: Plant leaf classification using probabilistic integration of shape, texture and margin features. Signal Process. Pattern Recogn. Appl. 5, 1 (2013)
Nguyen, T.T.N., Le, T.-L., Vu, H., Nguyen, H.-H., Hoang, V.-S.: A combination of deep learning and hand-designed feature for plant identification based on leaf and flower images. In: Król, D., Nguyen, N.T., Shirai, K. (eds.) ACIIDS 2017. SCI, vol. 710, pp. 223–233. Springer, Cham (2017). doi:10.1007/978-3-319-56660-3_20
Pavlidis, T.: A review of algorithms for shape analysis. Comput. Graph. Image Process. 7(2), 243–258 (1978)
Sebastian, T.B., Kimia, B.B.: Curves vs. skeletons in object recognition. Signal Process. 85(2), 247–263 (2005)
Söderkvist, O.: Computer vision classification of leaves from swedish trees (2001)
Teague, M.R.: Image analysis via the general theory of moments. JOSA 70(8), 920–930 (1980)
Terrades, O.R., Tabbone, S., Valveny, E.: A review of shape descriptors for document analysis. In: Ninth International Conference on Document Analysis and Recognition, ICDAR 2007, vol. 1, pp. 227–231. IEEE (2007)
Wahyono, Kurnianggoro, L., Yang, Y., Jo, K.H.: A similarity-based approach for shape classification using region decomposition. In: Huang, D.S., Jo, K.H., et al. (eds.) ICIC 2016. Lecture Notes in Computer Science, pp. 279–289. Springer, Cham (2016)
Wang, J., Bai, X., You, X., Liu, W., Latecki, L.J.: Shape matching and classification using height functions. Pattern Recogn. Lett. 33(2), 134–143 (2012)
Wang, X., Feng, B., Bai, X., Liu, W., Latecki, L.J.: Bag of contour fragments for robust shape classification. Pattern Recogn. 47(6), 2116–2125 (2014)
Yang, M., Kpalma, K., Ronsin, J.: A survey of shape feature extraction techniques (2008)
Yasseen, Z., Verroust-Blondet, A., Nasri, A.: Shape matching by part alignment using extended chordal axis transform. Pattern Recogn. 57, 115–135 (2016)
Zhang, D., Lu, G.: Review of shape representation and description techniques. Pattern Recogn. 37(1), 1–19 (2004)
Acknowledgement
This work was supported by the National Research Foundation of Korea (NRF) Grant funded by the Korean Government (2016R1D1A1A02937579).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Kurnianggoro, L., Wahyono, Filonenko, A., Jo, KH. (2017). Shape Classification Using Combined Features. In: Nguyen, N., Papadopoulos, G., Jędrzejowicz, P., Trawiński, B., Vossen, G. (eds) Computational Collective Intelligence. ICCCI 2017. Lecture Notes in Computer Science(), vol 10449. Springer, Cham. https://doi.org/10.1007/978-3-319-67077-5_53
Download citation
DOI: https://doi.org/10.1007/978-3-319-67077-5_53
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-67076-8
Online ISBN: 978-3-319-67077-5
eBook Packages: Computer ScienceComputer Science (R0)