Image Segmentation Through Contextual Clustering
Title | Image Segmentation Through Contextual Clustering |
Publication Type | Technical Report |
Year of Publication | 1998 |
Authors | Baraldi, A., Blonda P., Parmiggiani F., & Satalino G. |
Other Numbers | 1130 |
Keywords | Bayes' theorem, image segmentation, Markov Random Field |
Abstract | Several interesting strategies are adopted by the well-known Pappas clustering algorithm to segment smooth images. These include exploitation of contextual information to model both class conditional densities and a priori knowledge in a Bayesian framework. Deficiencies of this algorithm are that: i) it removes from the scene any genuine but small region; and ii) its feature-preserving capability largely depends on a user-defined smoothing parameter. This parameter is equivalent to a clique potential of a Markov Random Field model employed to capture known stochastic components of the labeled scene. In this paper a modified version of the Pappas segmentation algorithm is proposed to process smooth and noiseless images requiring enhanced pattern-preserving capability. In the proposed algorithm: iii) no spatial continuity in pixel labeling is enforced to capture known stochastic components of the labeled scene; iv) local intensity parameters, pixel labels, and global intensity parameters are estimated in sequence; and v) if no local intensity average is available to model one category in the neighborhood of a given pixel, then global statistics are employed to determine whether that category is the one closest to pixel data. Results show that our contextual algorithm can be employed: vi) in cascade to any noncontextual (pixel-wise) hard c-means clustering algorithm to enhance detection of small image features; and vii) as the initialization stage of any crisp and iterative segmentation algorithm requiring priors to be neglected on earlier iterations (such as the Iterative Conditional Modes algorithm). |
URL | http://www.icsi.berkeley.edu/ftp/global/pub/techreports/1998/tr-98-009.pdf |
Bibliographic Notes | ICSI Technical Report TR-98-009 |
Abbreviated Authors | A. Baraldi, P. Blonda, F. Parmiggiani, and G. Satalino |
ICSI Publication Type | Technical Report |