Shape Classification Based on Normalized Distance and Angle Histograms Using PNN

Full Text (PDF, 643KB), PP.65-72

Views: 0 Downloads: 0

Author(s)

Atefeh Goshvarpour 1 Hossein Ebrahimnezhad 1,* Ateke Goshvarpour 1

1. Computer Vision Lab, Faculty of Electrical Engineering, Sahand University of Technology, Tabriz, Iran

* Corresponding author.

DOI: https://doi.org/10.5815/ijitcs.2013.09.06

Received: 20 Nov. 2012 / Revised: 19 Mar. 2013 / Accepted: 12 May 2013 / Published: 8 Aug. 2013

Index Terms

Shape Classification, Distance Histogram, Feature Extraction, MPEG-7 Shapes

Abstract

This study presents an attempt to develop a reliable computerized algorithm, which could classify images into predetermined classes. For this purpose, the histogram of the normalized distance between each two points of the image (algorithm I) and the histogram of normalized distances between three points and the normalized angle of the image edge points (algorithm II) are analyzed. The probabilistic neural network (PNN) is implemented to do shape classification. Our proposed approach is tested on ten classes of MPEG-7 image database. It has been shown that feature extraction based on the distance histogram (algorithm I and algorithm II) is efficient due to its potential to preserve interclass and intra-class variation. In addition, these algorithms ensur invariance to geometric transformations (e.g. translation, rotation and scaling). The best classification accuracy is achieved by eight classes with the total accuracy of 90% and 92.5% for algorithm I and algorithm II, respectively. The reported experiment reveal that the proposed classification algorithm could be useful in the study of MPEG-7 shapes.

Cite This Paper

Atefeh Goshvarpour, Hossein Ebrahimnezhad, Ateke Goshvarpour, "Shape Classification Based on Normalized Distance and Angle Histograms Using PNN", International Journal of Information Technology and Computer Science(IJITCS), vol.5, no.9, pp.65-72, 2013. DOI:10.5815/ijitcs.2013.09.06

Reference

[1]Ramezani M., Ebrahimnezhad H. 3D Object Categorization Based on Histogram of Distance and Normal Vector Angles on Surface Points [J]. IEEE. Machine Vision and Image Processing (MVIP), 2011.

[2]Belongie S., Malik J., and Puzicha J. Shape Matching and Object Recognition Using Shape Context [J]. IEEE Trans. on PAMI, 2002, 24(24): 509-522.

[3]Grauman K., and Darrell T. Fast Contour Matching Using Approximate Earth Mover’s Distance [J]. CVPR. I, 2004, 220-227.

[4]Thayananthan A., Stenger B., Torr P.H.S., and Cipolla R. Shape Context and Chamfer Matching in Cluttered Scenes [J]. CVPR. I, 2003, 1063-6919.

[5]Ling H., and Jacobs D.W. Using the Inner-Distance for Classification of Articulated Shapes [J]. CVPR. II, 2005, 719-726.

[6]Lowe D. Distinctive Image Features from Scale-Invariant Keypoints [J]. IJCV. 2004, 60(2): 91-110.

[7]Mikolajczyk K., and Schmid C.A. Performance Evaluation of Local Descriptors [J]. IEEE Trans. on PAMI, 2005, 27(10): 1615- 1630. 

[8]Lazebnik S., Schmid C., and Ponce J.A. sparse texture representation using affine-invariant regions [J]. IEEE Trans. PAMI, 2005, 27(8): 1265-1278.

[9]Jain AK, Vailaya A. Shape-based retrieval: a case study with trademark image database [J]. Pattern Recognit, 1998, 31: 1369–90.

[10]Wei CH., Li Y., Chau W-Y., Li C-T. Trademark image retrieval using synthetic features for describing global shape and interior structure [J]. Pattern Recognit, 2008, 42: 386–94.

[11]Singh C., Pooja. Improving image retrieval using combined features of Hough transform and Zernike moments [J]. Optics and Lasers in Engineering, 2011, 49: 1384–1396.

[12]Scott C., Nowak R. Robust Contour Matching Via the Order-Preserving Assignment Problem [J]. IEEE Transactions on Image Processing, 2006, 15 (7): 1831-1838.

[13]Ling H., Jacobs D.W. Shape classification using the inner-distance [J]. IEEE Trans. Pattern Anal. Machine Intell, 2007, 29 (2): 286–299.

[14]Super B. Improving object recognition accuracy and speed through nonuniform sampling [C]. In: SPIE’03: Proceedings of the Society of Photo– Optical Instrumentation Engineers Conference, 2003, 5267: 228–239.

[15]Sebastian T.B., Klein P.N., Kimia B.B. On aligning curves [J]. IEEE Trans. Pattern Anal. Machine Intell, 2003, 25 (1): 116–125.

[16]ISO/IEC/JTC1/SC29/WG11: Core Experiment Results for Spatial Intensity Descriptor (CT4), MPEG document M5374, Maui, Dec. 1999.

[17]Nasreddine K., Benzinou A., Fablet R. Variational shape matching for shape classification and retrieval [J]. Pattern Recognition Letters, 2010, 3: 1650–1657.

[18]Demuth H., Beale M. Neural Network Toolbox. The MathWorks, Inc, 2000.

[19]Trunk G.V. A problem of dimensionality: A simple example [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1979, 1(3): 306–307.

[20]Bouguila N., Almakadmeh K., Boutemedjet S. A finite mixture model for simultaneous high-dimensional clustering, localized feature selection and outlier rejection [J]. Expert Systems with Applications, 2012, 39: 6641–6656.

[21]Brinkman D., Olver P.J. Invariant histograms. Amer Math Monthly, 2011, 118: 2-24.

[22]Boyle R., Havlac V., and Sonka M. Image Processing: Analysis and Machine Vision, (Pacific Grove: Brooks/Cole) 1999.

[23]Sapiro G. Geometric Partial Differential Equations and Image Analysis, (Cambridge: Cambridge University Press) 2001.

[24]Younes L. Optimal matching between shapes via elastic deformations [J]. Image Vision Comput, 2000, 17 (5): 381–389.

[25]Mokhtarian F., Abbasi S., Kittler J. Efficient and robust retrieval by shape content through curvature scale space [J]. Proceedings of International Workshop on Image DataBases and Multimedia Search, 1996, 35–42.

[26]Belongie S., Malik J., Puzicha J. Shape matching and object recognition using shape contexts [J]. IEEE Trans. Pattern Anal. Machine Intell, 2002, 24 (4): 509–522.

[27]Mokhtarian F., Bober M. Curvature Scale Space Representation: Theory, Applications, and MPEG-7 Standardization. Kluwer Academic Publishers, Norwell, MA, USA, 2003.

[28]Felzenszwalb P.F., Schwartz J.D. Hierarchical matching of deformable shapes [C]. CVPR’07: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2007, 1–8.