An Improved Approach of Maximal Similarity based Statistical Region Merging Using Nearest Neighbourhood Technique

Authors : Ruchika Kalal, Manish Tiwari.

Volume/Issue : Volume 4 - 2019, Issue 2 - February

Google Scholar :

Scribd :

Thomson Reuters ResearcherID :

The idea of this text is set up on a ruling elegance of hierarchical backside-up segmentation structures, called location merging tactics. The high target is committed to the concept, a statistical framework for the domain of unsupervised neighbourhood merging techniques. These techniques are characterised with the aid of using basic and nonparametric area devices, with either colour or texture homogeneity assumptions, or a hard and fast of revolutionary merging standards the usage of Bhattacharya similarity measure. The size consistency of the partitions is positive thru, (i) Deployment of knearest neighbour and imply shift algorithm for the base segmentation paintings and (ii) Use of a novel scale-focused merging order to limit the location homogeneity. Most massive mechanically extracted walls showcase the functionality to symbolize the semantic content material of the photo. Results are promising, outperforming in maximum indicators each shade and texture based totally segmentation techniques. The simulation results prove that the KNearest Neighbour based MSRM segmentation model is greater extended than suggest-shift method. Moreover, the experimental effects are comparatively analyzed the use of possibility random index, international consistent errors, version of statistics and top signal to noise ratio metrics.

Keywords : K-Nearest Neighbour, Similarioty Measure, Region Merging, Mean-Shift.


Paper Submission Last Date
29 - February - 2024

Paper Review Notification
In 1-2 Days

Paper Publishing
In 2-3 Days

Video Explanation for Published paper

Never miss an update from Papermashup

Get notified about the latest tutorials and downloads.

Subscribe by Email

Get alerts directly into your inbox after each post and stay updated.

Subscribe by RSS

Add our RSS to your feedreader to get regular updates from us.