Performance improvement in image clustering using local discriminant model and global integration

In this study, novel image clustering algorithm is investigated to improve the clustering performance. We have investigated this model and have achieved improved clustering performance by fine tuning the related model parameters. Yi Yang (2010) proposed clustering algorithm namely local discriminant model and global integration (LDMGI). Clustering parameters are number of nearest neighbours (k) and regularization parameter (λ). The reported parameters are k = 5 and the optimal value of λ selected from set {10-8 - 108} with step size of 102. It is observed that LDMGI clustering performance can be improved with different combination of k and λ. But no criteria exist for the selection of optimal k and λ for best clustering performance. We developed Improved-LDMGI by fine tuning the optimal value of λ in small step size of 0.25 while keeping k = 5 for all image dataset except handwritten image dataset. Significant performance improvement, on average of 7.0 percent, is observed.