Local Instability Problem of Image Segmentation Algorithms: Systematic Study and an Ensemble-Based Solution

Abstract

The region-based segmentation paradigm is a well known technique for image segmentation. In the¯rst part of this work the robustness of region-based algorithms is studied. It is shown that within a small parameter range, which leads to good segmentation results in the majority of cases, bad segmentation results may occur. In fact, such local instability is a problem of regionbased methods and reasons for its occurrence are discussed. In the second part of the work, an ensemble solution for this problem based on the median concept is proposed. Two variants, set median and generalized median, are presented and experimentally compared. Extensive experimental results demonstrate the potential of the proposed median approach for solving the instability problem.

Exact solution to median surface problem using 3D graph search and application to parameter space exploration

“…Then, the segmentation procedure is run for all the M parameter settings and the generalized median of the M segmentation results is computed. The rationale here is that in line with the ensemble paradigm, the median result tends to be a good one within the explored parameter subspace, as already successfully demonstrated for 2D contour detection [4] and region segmentation [21,22].…”

“…In our case an ensemble of K surfaces is averaged to achieve an "optimal" segmentation. This principle has been successfully validated for 2D contour detection [4] and image segmentation [21,22] before. In the current work this ensemble approach is demonstrated for parameter space exploration in the context of 3D surface segmentation.…”

“…In addition to terrain-like surfaces including single and multiple coupled surfaces, the median computation can also be applied to deal with tubular surfaces and closed surfaces (of both types). This work is motivated by the task of parameter space exploration without ground truth, which is an effective means of dealing with the difficult parameter problem and has been successfully applied to domains like 2D contour detection [4] and region segmentation [21,22]. Our median surface computation algorithm thus provides a useful tool for parameter exploration in 3D surface segmentation or 2D contour segmentation in a temporal context.…”

“…The median concept has been concretized to many domains including vectors [8], tensors [9], rotations [10], strings [11], graphs [12][13][14], clusterings [15,16], tree-like shapes [17], time series [18], ranking (permutation) [19], images (for atlas construction) [20], and image segmentations [21,22]. In [4] the 2D median contour problem is investigated.…”

Segmentation Ensemble - A Knowledge Reuse for Model Order Selection using Case-based Reasoning

“…Cluster ensemble has emerged as a powerful technique for dealing with several difficulties in clustering problems. Thus far, it has been used for dealing with 1) instability of clustering algorithms (Topchy et al, 2005;Franek et al, 2012); 2) sensitivity to noise, outliers or sample variations (Nguyen and Caruana, 2007); and 3) inaccuracy of individual clustering algorithms (Vega-pons and Ruiz-shulcloper, 2011; Strehl and Ghosh, 2002). Within this role, cluster ensemble integrates solutions from multiple different clusterings into a single better solution, normally, beyond what a single clustering algorithm can achieve.…”