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Research Papers

Gaussian and Gabor Filter Approach for Object Segmentation

[+] Author and Article Information
S. Thilagamani

Assistant Professor and Head
Department of Information Technology,
M. Kumarasamy College of Engineering,
Karur, Tamil Nadu 639 113, India
e-mail: thilagan1@yahoo.co.in

N. Shanthi

Professor and Dean
Department of Computer Science
and Engineering,
Nandha Engineering College,
Erode, Tamil Nadu 638052, India

Contributed by the Computers and Information Division of ASME for publication in the JOURNAL OF COMPUTING AND INFORMATION SCIENCE IN ENGINEERING. Manuscript received June 13, 2013; final manuscript received January 7, 2014; published online March 10, 2014. Assoc. Editor: Charlie C. L. Wang.

J. Comput. Inf. Sci. Eng 14(2), 021006 (Mar 10, 2014) (7 pages) Paper No: JCISE-13-1111; doi: 10.1115/1.4026458 History: Received June 13, 2013; Revised January 07, 2014

The problem of segmenting the object from the background is addressed in the proposed Gaussian and Gabor Filter Approach (GGFA) for object segmentation. An improved and efficient approach based on Gaussian and Gabor Filter reads the given input image and performs filtering and smoothing operation. The region occupied by the object is extracted from the image by performing various operations like bilateral filtering, Edge detection, Clustering, and Region growing. The proposed approach experimented on standard images taken from Caltech datasets, Corel Photo CDs, and Weizmann horse datasets show significantly improved results.

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References

Galun, M., Sharon, E., Basri, R., and Brandt, A., 2003, “Texture Segmentation by Multiscale Aggregation of Filter Responses and Shape Elements,” Proceedings of the IEEE Int'l Conference Computer Vision, pp. 716–723.
He, X., Zemel, R. S., and Carreira-Perpinan, M. A., 2004, “Multiscale Conditional Random Fields for Image Labeling”, Proceedings of IEEE CVPR.
Mignotte, M., 2012, “MDS-based Segmentation Model for the Fusion of Contour and Texture Cues in Natural Images,” Comput. Vis. Image Understanding, 116, pp. 981–990. [CrossRef]
Shotton, J., Winn, J. M., Rother, C., and Criminisi, A., 2006, “TextonBoost: Joint Appearance, Shape and Context Modeling for Multi-Class Object Recognition and Segmentation,” Proceedings of the Sixth European Conference Computer Vision, pp. 1–15.
Borenstein, E., and Malik, J., 2006, “Shape Guided Object Segmentation,” IEEE Conference on Computer Vision and Pattern Recognition.
Kim, J., and Grauman, K., 2012, “Shape Sharing for Object Segmentation,” Proceedings of European Conference on Computer Vision (ECCV).
Leibe, B., Leonardis, A., and Schiele, B., 2004, “Combined Object Categorization and Segmentation with an Implicit Shape Model,” Proceedings of the Workshop Sixth European Conference Computer Vision, pp. 17–32.
Munim, A. E., and Farag, A. A., 2005, “A Shape-Based Segmentation Approach: An Improved Technique using Level Sets,” Proceedings of the 10th IEEE Int'l ConferenceComputer Vision, pp. 930–935. [CrossRef]
Huang, R., Sang, N., Luo, D., and Tang, Q., 2011,”Image Segmentation Via Coherent Clustering in L*a*b* Color Space,” Pattern Recognit. Lett., 32, pp.891–902. [CrossRef]
Borenstein, E., Sharon, E., and Ullman, S., 2004, “Combining Top–Down and Bottom–Up Segmentation,” Proceedings of the IEEE Conference Computer Vision and Pattern Recognition.
Mori, G., 2005, “Guiding Model Search using Segmentation,” Proceedings of the 10th IEEE Int'l Conference Computer Vision, pp. 1417–1423.
Borenstein, E., and Ullman, S., 2004, “Learning to Segment,” Proceedings of the Eighth European Conference Computer Vision, pp. 315–328.
Russell, B. C., Freeman, W. T., Efros, A. A., Sivic, J., and Zisserman, A., 2006, “Using Multiple Segmentations to Discover Objects and Their Extent in Image Collections,” Proceedings of the IEEE Conference Computer Vision and Pattern Recognition, pp. 1605–1614.
Levin, A., and Weiss, Y., 2006, “Learning to Combine Bottom–Up and Top–Down Segmentation,” Proceedings of the Ninth European Conference Computer Vision, pp. 581–594.
Cour, T., and Shi, J., 2007, “Recognizing Objects by Piecing Together the Segmentation Puzzle,” Proceedings of the IEEE Conference Computer Vision and Pattern Recognition, pp. 1–8.
Sharon, E., Brandt, A., and Basri, R., 2001, “Segmentation and Boundary Detection Using Multiscale Intensity Measurements,” Proceedings of the IEEE Conference Computer Vision and Pattern Recognition, pp. 469–476.
Zhao, L., and Davis, L. S., 2005, “Closely Coupled Object Detection and Segmentation,” Proceedings of the 10th IEEE Int'l Conference Computer Vision, pp. 454–461.
Yu, S. X., and Shi, J., 2003, “Object-Specific Figure-Ground Segregation,” Proceedings of the IEEE Conference Computer Vision and Pattern Recognition, pp. 39–45.
Cao, L., and Fei-Fei, L., 2007, “Spatially Coherent Latent Topic Model for Concurrent Segmentation and Classification of Objects and Scenes,” Proceedings of the 11th IEEE Int'l Conference Computer Vision, pp. 1–8.
Harris, C., and Stephens, M., 1988, “A Combined Corner and Edge Detection,” Proceedings of the Fourth Alvey Vision Conference, pp. 147–151.
Borenstein, E., and Ullman, S., 2002, “Class-Specific, Top–Down Segmentation,” Proceedings of the Seventh European Conference Computer Vision, pp. 109–124.
Liu, G., Lin, Z., Yu, Y., and Tang, X., 2010, “Unsupervised Object Segmentation with a Hybrid Graph Model(HGM),” IEEE transactions on pattern Analysis and Machine Intelligence, 32(5), pp. 910–924. [CrossRef] [PubMed]
Winn, J. M., and Jojic, N., 2005, “Locus: Learning Object Classes with Unsupervised Segmentation,” Proceedings of the 10th IEEE Int'l Conference Computer Vision, pp. 756–763.

Figures

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Fig. 2

Performance of HGM [22] for sample standard object classes

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Fig. 3

Performance of GGFA for sample standard object classes

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Fig. 4

Accuracy on the standard image data set

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Fig. 5

GGFA versus [5,15] based on recall values

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Fig. 6

GGFA-intermediate operations on old car object class

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Fig. 7

Evaluations of GGFA on old car object class

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