Publication Details
Title: Image Segmentation Through Contextual Clustering
Author: A. Baraldi, P. Blonda, F. Parmiggiani, and G. Satalino
Group: ICSI Technical Reports
Date: March 1998
PDF: ftp://ftp.icsi.berkeley.edu/pub/techreports/1998/tr-98-009.pdf
Overview:
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). Keywords: Markov Random Field, Bayes' theorem, image segmentation.
Bibliographic Information:
ICSI Technical Report TR-98-009
Bibliographic Reference:
A. Baraldi, P. Blonda, F. Parmiggiani, and G. Satalino. Image Segmentation Through Contextual Clustering. ICSI Technical Report TR-98-009, March 1998
Author: A. Baraldi, P. Blonda, F. Parmiggiani, and G. Satalino
Group: ICSI Technical Reports
Date: March 1998
PDF: ftp://ftp.icsi.berkeley.edu/pub/techreports/1998/tr-98-009.pdf
Overview:
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). Keywords: Markov Random Field, Bayes' theorem, image segmentation.
Bibliographic Information:
ICSI Technical Report TR-98-009
Bibliographic Reference:
A. Baraldi, P. Blonda, F. Parmiggiani, and G. Satalino. Image Segmentation Through Contextual Clustering. ICSI Technical Report TR-98-009, March 1998