Kernalized average entropy and density based spatial clustering with noise

Image clustering is one of the key technologies for image processing. Most image clustering methods based on density algorithms encountered with challenges including robust salient region extraction, cluster centre identification and noise. To address these issues, different density-based clustering methods were used. To accommodate this constraint, this paper proposes a method named Kernalized Average Entropy and Density-based Spatial Clustering (KAE-DSC). The KAE-DSC method efficiently extracts the robust salient region for texture and image database using Spatial Candidate Region Detection. Finally, Spatial Density-based Clustering with Noise is used to differentiate between core, non-core and noisy region for grouping similar region. By identifying the three different regions, efficient prediction is said to be made for the given texture and medical images. KAE-DSC method is compared with several other methods based on several evaluation indexes to testify that the proposed KAE-DSC is outperformed. Abundant experimental results proved that KAE-DSC is robust to parameters, which can automatically determine the number of clustering and improve the accuracy of clustering with minimum time complexity.

Automated summary

The proposed method, KAE-DSC, efficiently extracts robust salient regions from texture and image databases using Spatial Candidate Region Detection, and differentiates between different regions through Spatial Density-based Clustering with Noise for image clustering. Experimental results demonstrate that KAE-DSC outperforms other methods based on multiple evaluation indexes and exhibits strong parameter robustness.