Open Access Open Access  Restricted Access Subscription or Fee Access

A Detailed Analysis on Image Segmentation and its Open Issues

Farheen K. Siddiqui, Vineet Richhariya

Abstract


Image segmentation is vital step in the process of image processing, which is at the middle layer of image engineering. An outline of the development of image segmentation in the recent years is analyzed. This paper gives a deeper analysis of the topic along with the various techniques being worked out in the recent years. Several thousands of segmentation algorithms have been designed and applied for various applications, and this number has increased steadily making it quite hard to work out a comprehensive survey and a suitable classification scheme for hierarchical categorizing the whole techniques. Each method has its own advantages and disadvantages to be use in different problem areas. Accuracy, complexity, efficiency and interactivity of a segmentation method should all be the considered factors. The domain of image segmentation is still immature. Many unsettled problems need to be defined and solved in this area. It is firmly believed that this domain will greatly advance in the future. This evaluation may prove to be an essential step for the development and improvement of new techniques in this area.

Keywords


Segmentation, Image Processing, Clustering, Region Based, Threshold Based, Watershed

Full Text:

PDF

References


Solomon C.J. and Breckon T.P., “Fundamentals of Digital Image Processing: A Practical Approach with Examples in Matlab”, Wiley-Blackwell,2010.

T. Malisiewicz and A. A. Efros, “ Improving spatial support for objects via multiple segmentations,” in Proc. BMVC, 2007.

Huang Min,Sun bo,Xi Jianqing”An Optimized image retrieval method based on Hierarchical clustering and genetic algorithm”I’ntl forum on Information technology and applications,978-0-7695-3600-2/09-IEEE,2009.

Li Wenchao Zhou Yong Xia Shixiong China Univ. of Min. & Technol., Xuzhou, “A Novel Clustering Algorithm Based on Hierarchical and K-means Clustering” Print ISBN: 978-7-81124-055-9, On page(s): 605, 2009.

Irani, A.A.Z. Belaton, “A K-means Based Generic Segmentation System B.Dept. of Comput. Sci., Univ. Sains Malaysia, Nibong Tebal, Malaysia Print ISBN: 978-0-7695-3789-4 On page(s): 300 – 307, 2009.

Isa, N.A.M.; Salamah, S.A.; Ngah, U.K.; Sch. of Electr. & Electron. Eng., Univ. Sains Malaysia, Nibong Tebal, Malaysia , “Adaptive fuzzy moving K-means clustering algorithm for image segmentation” ISSN: 0098-3063 ,On page(s): 2145 – 2153, 2009.

Jianbo Shi Malik, J.Robotics Inst., Carnegie Mellon Univ., Pittsburgh, PA, “ Normalized cuts and image segmentation”, ISSN: 0162-8828, On page(s): 888 – 905,2000.

LI XiaoBin and TIAN Zheng”Multiscale stochastic hierarchical image segmentation by spectral clustering”,Sci China Ser F-Inf Sci, vol.50 |no.2|pg.no-198-211,2007.

Li Yaoyong and Li Shuguang, “Two-Dimensional Arimoto Entropy Image Thresholding based on Ellipsoid Region Search Strategy”, Print ISBN:978-1-4244-7871-2, Multimedia Technology (ICMT) International Conference, 2010.

Guangyou Jiang and Gewen Kang , “A threshold segmentation algorithm based on neighbourhood characteristics”, Print ISBN: 978-1-4244-8158-3, Electronic Measurement & Instruments (ICEMI), 10th International Conference, 2011.

Mengxing Huang , Wenjiao Yu and Donghai Zhu, “An Improved Image Segmentation Algorithm Based on the Otsu Method”, Print ISBN: 978-1-4673-2120-4, Software Engineering, Artificial Intelligence, Networking and Parallel & Distributed Computing (SNPD), 13th ACIS International Conference, 2012.

Tang and Jun, “A color image segmentation algorithm based on region growing”, Print ISBN: 978-1-4244-6347-3, Computer Engineering and Technology (ICCET), 2nd International Conference, 2010.

Li, Wenguo, “Multi-threshold color image segmentation based on region growing”, Print ISBN: 978-1-4244-6582-8, Intelligent Computing and Intelligent Systems (ICIS), IEEE International Conference, 2010.

Vojodi Hakimeh, Moghadam and Amir-Masoud Masoud Eftekhari , “A supervised evaluation method based on region shape descriptor for image segmentation algorithm”, Print ISBN: 978-1-4673-1478-7, Artificial Intelligence and Signal Processing (AISP), 16th CSI International Symposium,2012.

P. Felzenszwalb and D. Huttenlocher,“Efficient graph-based image segmentation,” Int. J. Comput. Vis., vol. 59, no. 2, pp. 167–181, Sep. 2004.

L. Zhang and Qiang Ji, “Image Segmentation with a Unified Graphical Model,”IEEE Transactions Pattern Anal. Mach. Intell., vol. 32, no. 8, pp. 1406–1425, August 2010.

Lei Zhang, Zhi Zeng, and Qiang Ji “Probabilistic Image Modeling With an Extended Chain Graph for Human Activity Recognition and Image Segmentation”, IEEE Transaction on Image Processing, VOL. 20, NO. 9, September 2011.

Parisot Sarah, Duffau Hugues, Chemouny Stéphane, Paragios Nikolaos K. , “Graph-based detection, segmentation & characterization of brain tumors” , Computer Vision and Pattern Recognition (CVPR), IEEE Conference, 2012.

Hao Wei, Zheng Sheng, Ye Shu-zhi, “One improved watershed transform for medical image segmentation”, Computer Application and System Modeling (ICCASM), International Conference, 2010.

Zhang Gui-Mei , Zhou Ming-Ming, Chu Jun, Miao Jun , “Labeling watershed algorithm based on morphological reconstruction in color space”, Haptic Audio Visual Environments and Games (HAVE), IEEE International Workshop, 2011.

Allili Madjid, Bentabet Layachi Chen Yan, “A novel topology based watershed segmentation”, Information Science, Signal Processing and their Applications (ISSPA), 11th International Conference, 2012.

Rahman, Md Mahbubur, “An unsupervised natural image segmentation algorithm using mean histogram features”, Computer and Information Technology (ICCIT), 14th International Conference, 2011.

Gao Song, Zhang Chengcui and Chen Wei-Bang, “An improvement of color image segmentation through projective clustering”, Information Reuse and Integration (IRI), IEEE 13th International Conference, 2012.

Zhang Guangyun, Jia Xiuping,Kwok Ngai M., “Super pixel based remote sensing image classification with histogram descriptors on spectral and spatial data”, Geoscience and Remote Sensing Symposium (IGARSS), IEEE International, 2012


Refbacks

  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.