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Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
13, Universitatii Street
Suceava - 720229
ROMANIA

Print ISSN: 1582-7445
Online ISSN: 1844-7600
WorldCat: 643243560
doi: 10.4316/AECE


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  1/2019 - 6

Efficient Shape Classification using Zernike Moments and Geometrical Features on MPEG-7 Dataset

ABBAS, S., FARHAN, S. See more information about  FARHAN, S. on SCOPUS See more information about  FARHAN, S. on SCOPUS See more information about FARHAN, S. on Web of Science, FAHIEM, M. A. See more information about  FAHIEM, M. A. on SCOPUS See more information about  FAHIEM, M. A. on SCOPUS See more information about FAHIEM, M. A. on Web of Science, TAUSEEF, H. See more information about TAUSEEF, H. on SCOPUS See more information about TAUSEEF, H. on SCOPUS See more information about TAUSEEF, H. on Web of Science
 
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Download PDF pdficon (1,361 KB) | Citation | Downloads: 278 | Views: 542

Author keywords
classification algorithms, feature extraction, image classification, shape

References keywords
shape(23), recognition(12), pattern(11), machine(9), image(9), classification(8), learning(7), descriptors(7), retrieval(6), content(5)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2019-02-28
Volume 19, Issue 1, Year 2019, On page(s): 45 - 50
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2019.01006
Web of Science Accession Number: 000459986900006
SCOPUS ID: 85064223642

Abstract
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There is an urgent need and demand for manipulating images to extract useful information from them. In every field, whether it is biotechnology, botany, medical, robotics or machinery, the demand for extracting useful aspects of a specific targeted image is growing. Effective systems and applications have been introduced for this purpose: CBIR and MPEG-7 are most common applications. Shape extraction and recognition is used in image retrieval and matching. Complex objects can be identified and classified by extracting their shape. This paper proposes an efficient algorithm for shape classification. Analyses are made on MPEG-7 dataset using 1400 images belonging to 70 classes. Zernike Moments descriptor and geometrical features are used for classification purposes. Cross validation and percentage split are used to evaluate the proposed scheme. Experimental results proved the efficiency of the proposed approach with an accuracy of 92.45 percent on the challenging dataset.


References | Cited By  «-- Click to see who has cited this paper

[1] T. Dharani and I. L. Aroquiaraj, "A survey on content based image retrieval," in Pattern Recognition, Informatics and Mobile Engineering (PRIME), 2013 IEEE Conference on, 2013, pp. 485-490.
[CrossRef] [SCOPUS Times Cited 47]


[2] C. Iakovidou, N. Anagnostopoulos, A. C. Kapoutsis, Y. Boutalis and S. A. Chatzichristofis , "Searching images with MPEG-7 (& MPEG-7-like) powered localized descriptors: the SIMPLE answer to effective content based image retrieval," in Content-Based Multimedia Indexing, 2014 IEEE 12th International Workshop on, 2014, pp. 1-6.
[CrossRef]


[3] M. Anvaripour and H. Ebrahimnezhad, "Accurate object detection using local shape descriptors," Pattern Analysis and Applications, vol. 18, no. 2, pp. 277-295, 2015.
[CrossRef]


[4] S. Seth, P. UpaRedhyay, R. Shroff and R. Komatwar, "Review of content based image retrieval systems," International Journal of Engineering Trends and Technology, vol. 19, no. 4, pp. 178-181, 2015.

[5] L. Zhao, Q. Peng and B. Huang, "Shape matching algorithm based on shape contexts," IET Computer Vision, vol. 9, no. 5, pp. 681-690, 2015.
[CrossRef] [Web of Science Times Cited 8] [SCOPUS Times Cited 11]


[6] A. Barman and P. Dutta, "Facial expression recognition using shape signature feature," in Research in Computational Intelligence and Communication Networks, 2017 IEEE Third International Conference on, 2017, pp. 174-179.
[CrossRef] [SCOPUS Times Cited 3]


[7] G. Zhang and D. Lu, "Review of shape representation and description techniques," Pattern Recognition, vol. 37, no. 1, pp. 1-19, 2004.
[CrossRef] [Web of Science Times Cited 956] [SCOPUS Times Cited 1271]


[8] S. Piérard, A. Lejeune and M. V. Droogenbroeck, "Boosting shape classifiers accuracy by considering the inverse shape," Journal of Pattern Recognition Research, vol. 11, no. 1, pp. 41-54, 2016.
[CrossRef] [Web of Science Times Cited 1]


[9] S. Sharma and P. Khanna, "Computer-aided diagnosis of malignant mammograms using zernike moments" Journal of Digital Imaging, vol. 28, no. 1, pp. 77-90, 2015.
[CrossRef] [Web of Science Times Cited 38] [SCOPUS Times Cited 47]


[10] C. Lin, C. M. Pun, C. M. Vong and D. Adjeroh, "Efficient shape classification using region descriptors," Multimedia Tools and Applications, vol. 76, no. 1, pp. 83-102, 2017.
[CrossRef] [Web of Science Times Cited 2] [SCOPUS Times Cited 3]


[11] M. Bicego and P. Lovato, "A bioinformatics approach to 2D shape classification," Computer Vision and Image Understanding, vol. 145, pp. 59-69, 2016.
[CrossRef] [Web of Science Times Cited 9] [SCOPUS Times Cited 13]


[12] S. Battiato, G. M. Farinella, O. Giudice and G. Puglisi, "Aligning shapes for symbol classification and retrieval," Multimedia Tools and Applications, vol. 75, no. 10, pp. 5513-5531, 2016.
[CrossRef] [Web of Science Times Cited 2] [SCOPUS Times Cited 6]


[13] S. Escalera, A. Fornés, O. Pujol, P. Radeva, G. Sánchez, et al., "Blurred shape model for binary and grey-level symbol recognition," Pattern Recognition Letters, vol. 30, no. 15, pp. 1424-1433, 2009.
[CrossRef] [Web of Science Times Cited 39] [SCOPUS Times Cited 61]


[14] S. Belongie, J. Malik and J. Puzicha, "Shape matching and object recognition using shape contexts," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, no. 4, pp. 509-522, 2002.
[CrossRef] [Web of Science Times Cited 3396] [SCOPUS Times Cited 4608]


[15] D. Sharvit, J. Chan, H. Tek and B. B. Kimia, "Symmetry-based indexing of image databases," in Content-Based Access of Image and Video Libraries, 1998 IEEE Workshop on, pp. 56-62, 1998.
[CrossRef] [Web of Science Times Cited 27] [SCOPUS Times Cited 52]


[16] H. Ling and D. W. Jacobs, "Shape classification using the inner-distance," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 29, no. 2, pp. 286-299, 2007.
[CrossRef] [Web of Science Times Cited 624] [SCOPUS Times Cited 808]


[17] X. Bai, B. Wang, C. Yao, W. Liu and Z. Tu, "Co-transduction for shape retrieval," IEEE Transactions on Image Processing, vol. 21, no 5, pp. 2747-2757, 2012.
[CrossRef] [Web of Science Times Cited 62] [SCOPUS Times Cited 78]


[18] L. J. Latecki, R. Lakamper and T. Eckhardt, "Shape descriptors for non-rigid shapes with a single closed contour," in Computer Vision and Pattern Recognition, 2000 IEEE Conference on, 2000, pp. 424-429.
[CrossRef]


[19] C. Lin and C. M. Pun, "Robust region descriptors for shape classification," in Computer Graphics, Imaging and Visualization, 2016 International Conference on, 2016, pp. 269-272.
[CrossRef] [Web of Science Times Cited 1] [SCOPUS Times Cited 1]


[20] C. Pillai, "A survey of shape descriptors for digital image processing," International Journal of Computer Science and Information Technology and Security, vol. 3, no. 1, pp. 44-50, 2013.

[21] I. K. Kazmi, L. You and J. J. Zhang, "A survey of 2D and 3D shape descriptors," in Computer Graphics Imaging and Visualization, 2013 Tenth International Conference on, 2013, pp. 1-10.
[CrossRef] [SCOPUS Times Cited 29]


[22] W. Y. Kim and Y. S. Kim, "A region-based shape descriptor using zernike moments," Signal Processing: Image Communication, vol. 16, no. 1, pp. 95-102, 2000.
[CrossRef] [Web of Science Times Cited 220] [SCOPUS Times Cited 287]


[23] M. Murat, S.W. Chang, A. Abu, H. J. Yap and K. T. Yong, "Automated classification of tropical shrub species: a hybrid of leaf shape and machine learning approach," PeerJ, vol. 5, no. e3792, pp. 1-23, 2017.
[CrossRef] [Web of Science Times Cited 1] [SCOPUS Times Cited 3]


[24] M. Yang, K. Kpalma and J. Ronsin, "A survey of shape feature extraction techniques," Pattern Recognition Techniques, Technology and Applications, Austria, pp. 43-90, 2008.

[25] S. G. Wu, F. S. Bao, E. Y. Xu, Y. X. Wang, Y. F. Chang, et al., "A leaf recognition algorithm for plant classification using probabilistic neural network," in Signal Processing and Information Technology, 2007 IEEE International Symposium on, 2007, pp. 11-16.
[CrossRef] [SCOPUS Times Cited 348]


[26] J. G. Cleary and L. E. Trigg, "K*: an instance-based learner using an entropic distance measure," in Machine Learning, 1995 12th International Conference on, 1995, pp. 108-114.
[CrossRef]


[27] D. W. Aha, D. Kibler and M. K. Albert, "Instance-based learning algorithms," Machine Learning, vol. 6, no. 1, pp. 37-66, 1991.
[CrossRef] [Web of Science Times Cited 2313]


[28] E. Bauer and R. Kohavi, "An empirical comparison of voting classification algorithms: bagging, boosting, and variants," Machine Learning, vol. 36, no. 1, pp. 105-139, 1999.
[CrossRef] [Web of Science Times Cited 1169]


[29] T. K. Ho, "The random subspace method for constructing decision forests," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 20, no. 8, pp. 832-844, 1998.
[CrossRef] [SCOPUS Times Cited 3037]


[30] C. E. Rasmussen, "Gaussian processes in machine learning," Advanced Lectures on Machine Learning, Germany, pp. 63-71, 2004.
[CrossRef]


[31] M. Hall, E. Frank, G. Holmes, B. Pfahringer, P. Reutemann, et al., "The WEKA data mining software: an update," ACM SIGKDD Explorations Newsletter, vol. 11, no. 1, pp. 10-18, 2009.
[CrossRef]


[32] S. Escalera, A. Fornes, O. Pujol, J. Llados and P. Radeva, "Circular blurred shape model for multiclass symbol recognition," IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), vol. 41, no. 2, pp. 497-506, 2011.
[CrossRef] [Web of Science Times Cited 21] [SCOPUS Times Cited 32]


[33] X. Yang, S. Koknar-Tezel and L. J. Latecki, "Locally constrained diffusion process on locally densified distance spaces with applications to shape retrieval," in Computer Vision and Pattern Recognition, 2009 IEEE Conference on, 2009, pp. 357-364.
[CrossRef]




References Weight

Web of Science® Citations for all references: 8,889 TCR
SCOPUS® Citations for all references: 10,745 TCR

Web of Science® Average Citations per reference: 261 ACR
SCOPUS® Average Citations per reference: 316 ACR

TCR = Total Citations for References / ACR = Average Citations per Reference

We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more

Citations for references updated on 2019-12-12 11:57 in 630 seconds.




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Faculty of Electrical Engineering and Computer Science
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