/MBHC_WMM

Implementation of the method: Model Based Hierarchical Clustering using Watson Mixture Model (MBHC-WMM)

Primary LanguageMatlab

MBHC_WMM

Implementation of the clustering method: Model Based Hierarchical Clustering using Watson Mixture Model (MBHC-WMM)

  • The MBHC-WMM method is an automatic method to cluster 3 dimensional axial data. This repo provides GUI demo with MATLAB code to do the following tasks:
    a. load 3 dimensional axial (labeled/unlabeled) data and display them.
    b. Generate/Synthesize 3 dimensional axial (labeled/unlabeled) data with given number of groups/cluster.
    c. Automatically cluster 3 dimensional axial data.

How to use demo (tested in Matlab2017b):

Run the MATLAB file name: mbc_wmm.m
Load data/samples files name: S_10000_5_Cl_1.mat or S_10000_5_Cl_45.mat
OR Generate 3D samples with specific number of cluster (edit text box - Num Class)

Application:

This clustering method has been used to cluster image normals (3D directional unit vectors) for analyzing depth/3D images. For details and other possible applications please see the reference.

Advantage:

This clustering method does not require to specify the number of clusters. It automatically determines it (see the reference).

Limitations:

This clustering method is limited to cluster 3 Dimensional directional samples only.

Extensions and scopes:

You can extend this method for higher dimensional samples.

Reference:

  1. Abul Hasnat, Olivier Alata and Alain Tremeau, Unsupervised Clustering of Depth Images using Watson Mixture Model, In Int. Conference on Pattern Recognition (ICPR) 2014, Stockholm, Sweden. pdf download

  2. Abul Hasnat, Olivier Alata and Alain Trmeau, Joint Color-Spatial-Directional clustering and Region Merging (JCSD-RM) for unsupervised RGB-D image segmentation, In IEEE Trans. on Pattern Analysis and Machine Intelligence (TPAMI), Vol 38, Issue 11, pages 2255 - 2268, 2015. pdf download