NDLI: Divergence based SLIC

Content Provider	IET Digital Library
Author	Gupta, A. K. Seal, A. Khanna, P.
Abstract	The success of cluster analysis for revealing natural grouping in a dataset depends heavily on the chosen dissimilarity measure. Recently, several attempts have been made to replace the popular Euclidean distance measure for dissimilarity with divergences that are known to disobey triangular inequality. In this Letter, a representative partitioning based superpixel algorithm called Simple Linear Iterative Clustering (SLIC) is experimented with a divergence measure. In particular, the Jeffery divergence is employed for dissimilarity computation between colour-image plane space vectors while performing cluster assignment. Despite being a non-metric, the Jeffery divergence based SLIC has shown better boundary adherence performance on BSDS500 dataset as compared to the conventional SLIC algorithm.
Starting Page	783
Ending Page	785
Page Count	3
ISSN	00135194
Volume Number	55
e-ISSN	1350911X
Issue Number	Issue 14, Jul (2019)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/el/55/14
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/el.2019.1092
Journal	Electronics Letters
Publisher Date	2019-05-24
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	BSDS500 Dataset Cluster Analysis Cluster Assignment Colour-image Plane Space Vector Computer Vision And Image Processing Technique Dissimilarity Computation Dissimilarity Measure Divergence Based SLIC Divergence Measure Image Colour Analysis Image Resolution Interpolation And Function Approximation Iterative Method Jeffery Divergence Numerical Analysis Optical, Image And Video Signal Processing Pattern Clustering Representative Partitioning Simple Linear Iterative Clustering Superpixel Algorithm Triangular Inequality
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering

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