Object-Based Image Segmentation using Fuzzy Clustering

Existing shape-based clustering algorithms, including fuzzy k-rings, fuzzy k-elliptical, circular c-shell, and fuzzy c-shell ellipsoidal are all designed to segment regular geometrically shaped objects such as circles, ellipses or combination of both. These algorithms however, are unsuitable for segmenting arbitrary-shaped objects, so in an attempt to address this issue, a fuzzy image segmentation of generic shaped clusters (FISG) algorithm was introduced that integrated generic shape information into the segmentation framework. It however, had a number of limitations relating to the mathematical derivation of the updated contour radius, the initial shape representation, and the impact of overlapping clusters. This paper proposes a new object based segmentation using fuzzy clustering (OSF) algorithm that solves these drawbacks by controlling the scaling of original shape, securing a better initial shape representation and avoids cluster overlapping, with both qualitative and quantitative results confirming the improved overall segmentation performance

Robust object segmentation using split-and-merge

In spite of simplicity and effectiveness in segmenting homogeneous regions in an image, Split-and-Merge (SM) algorithm is unable to segment all types of objects due to huge number of objects with myriad variations among them and due to high dependability on the threshold values used in splitting and merging techniques. Addressing these issues, a novel Robust Object Segmentation using Split-and-Merge (ROSSM) is proposed in this paper considering image feature stability, inter- and intra-object variability, and human visual perception. The qualitative analysis proves the superior performance of ROSSM in comparison with the basic SM algorithm and a recently developed shape-based fuzzy clustering algorithm namely Object-based image Segmentation using Fuzzy clustering (OSF)

Pattern based object segmentation using split and merge

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.IEEE International Conference on Fuzzy System

Object segmentation based on split and merge algorithm

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.IEEE Region 10 Annual International Conference, Proceedings/TENCO