Object segmentation based on split and merge algorithm
- Authors: Faruquzzaman, A. B. M. , Paiker, Nafize , Arafat, Jahidul , Karim, Ziaul , Ali, Mortuza
- Date: 2008
- Type: Text , Conference proceedings
- Relation: 2008 IEEE Region 10 Conference, TENCON 2008; Hyderabad; India; 19th -21st Nov published in IEEE Region 10 Annual International Conference, Proceedings/TENCON p. 1-4
- Full Text: false
- Reviewed:
- Description: Image segmentation is a feverish issue as it is a challenging job and most digital imaging applications require it as a preprocessing step. Among various algorithms, although split and merge (SM) algorithm is highly lucrative because of its simplicity and effectiveness in segmenting homogeneous regions, however, it is unable to segment all types of objects in an image using a general framework due to not most natural objects being homogeneous. Addressing this issue, a new algorithm namely object segmentation based on split and merge algorithm (OSSM) is proposed in this paper considering image feature stability, inter- and intra-object variability, and human visual perception. The qualitative analysis has been conducted and the segmentation results are compared with the basic SM algorithm and a shape-based fuzzy clustering algorithm namely object based image segmentation using fuzzy clustering (OSF). The OSSM algorithm outperforms both the SM and the OSF algorithms and hence increases the application area of segmentation algorithms.
- Description: IEEE Region 10 Annual International Conference, Proceedings/TENCON
Pattern based object segmentation using split and merge
- Authors: Karim, Ziaul , Paiker, Nafize , Ali, M. Ameer , Sorwar, Golam , Islam, M. M.
- Date: 2009
- Type: Text , Conference proceedings
- Relation: 2009 IEEE International Conference on Fuzzy Systems; Jeju Island; South Korea; 20th- 24th August 2009 p. 2166-2169
- Full Text: false
- Reviewed:
- Description: Split and Merge (SM) algorithm is a well recognized algorithm for segmenting homogeneous regions in an image. Though SM algorithm is simple and easy, this algorithm is unable to segment all type objects in an image successfully due to huge variations among the objects in size, shape, color and intensity. Moreover, the SM algorithm is also highly dependent on threshold values used for split and merge stages. Addressing these issues, a new algorithm namely pattern based object segmentation using split and merge (PSM) considering the basic SM algorithm, the region stability, and the patterns for object extraction. The experimental results prove the superior segmentation performance of the PSM algorithm in comparison with the basic SM algorithm, suppressed fuzzy c-means (SFCM), and object based image segmentation using fuzzy clustering (FISG). ©2009 IEEE.
- Description: IEEE International Conference on Fuzzy Systems