NDLI: Detecting image communities

Content Provider	IEEE Xplore Digital Library
Author	Esen, E. Ozkan, S. Atil, I. Arabaci, M.A. Tankiz, S.
Copyright Year	2014
Description	Author affiliation: TUBITAK UZAY Image Process. Group, METU Balgat, Ankara, Turkey (Esen, E.; Ozkan, S.; Atil, I.; Arabaci, M.A.; Tankiz, S.)
Abstract	In this work, we propose a novel community detection method that is specifically designed for image communities. We define image community as a coherent subgroup of images within a large set of images. In order to detect image communities, we construct an image graph by utilizing visual affinity between each image pair and then prune most of the links. Instead of affinity values, we prefer ranking of neighboring images and get rid of range mismatch of affinity values. The resulting directed graph is processed to detect the image communities by using the proposed deterministic method. The proposed method is compared against state-of-the-art community detection methods that can operate on directed graphs. In the experiments, we use various sets of images for which ground truths are determined manually. The results indicate that our method significantly outperforms the compared state-of-the-art methods. Furthermore, the proposed method appears to have a consistent performance between sets unlike the compared methods. We believe that the proposed community detection method can be successfully utilized in many different applications.
Sponsorship	IEEE
Starting Page	1
Ending Page	4
File Size	2967208
Page Count	4
File Format	PDF
ISBN	9781479939909
ISSN	19493991
DOI	10.1109/CBMI.2014.6849841
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-06-18
Publisher Place	Austria
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Measurement Computers Visualization Image edge detection Communities Detection algorithms Equations
Content Type	Text
Resource Type	Article

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