NDLI: Image segmentation using linked mean-shift vectors with region attribution analysis

Content Provider	IEEE Xplore Digital Library
Author	Hanjoo Cho Young Hwan Kim
Copyright Year	2015
Description	Author affiliation: Dept. of Electr. Eng., POSTECH, Pohang, South Korea (Hanjoo Cho; Young Hwan Kim)
Abstract	This paper proposes a novel linked mean-shift algorithm that considers region attribution in a cluster merging process. Mean-shift based image segmentation suffers from its extreme computational complexity, despite of its outstanding segmentation accuracy. To resolve this problem, the linked mean-shift algorithm that removes the iterative process in the mean-shift process was introduced. However, the approximation in the linked-mean-shift algorithm gives rise to unwanted merging of the clusters that should not be merged. To prevent the unwanted merging, the proposed algorithm analyzes region attribution, then, in the merging process, applies strict condition to the clusters that have dissimilar attribution than the clusters that have similar attribution. In experiments, the proposed algorithm improved segmentation accuracy than the linked mean-shift algorithm, while retained twenty times faster speed than the mean-shift algorithm. Furthermore, the experimental results for variation of processing time showed the proposed algorithm can provide much settled throughput than the mean-shift algorithm.
Starting Page	188
Ending Page	191
File Size	302301
Page Count	4
File Format	PDF
e-ISBN	9781479982295
DOI	10.1109/PRIME.2015.7251366
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-06-29
Publisher Place	UK
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Algorithm design and analysis Image segmentation Histograms mean-shift algorithm Accuracy Merging image segmentation Clustering algorithms Approximation algorithms
Content Type	Text
Resource Type	Article

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