Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/1176
Title: Complex networks driven salient region detection based on superpixel segmentation
Authors: Aksaç, Alper
Özyer, Tansel
Alhajj, Reda
143116
Keywords: Superpixel
Segmentation
Salient Region Detection
Saliency Map
Complex Networks
Issue Date: Jun-2017
Publisher: Elsevier Science Ltd.
Source: Aksac, A., Ozyer, T., & Alhajj, R. (2017). Complex networks driven salient region detection based on superpixel segmentation. Pattern Recognition, 66, 268-279.
Abstract: In this paper, we propose an efficient method for salient region detection. First, the image is decomposed by using superpixel segmentation which groups similar pixels and generates compact regions. Based upon the generated superpixels, similarity between the regions is calculated by benefiting from color, location, histogram, intensity, and area information of each region as well as community identification via complex networks theory in the over-segmented image. Then, contrast, distribution and complex networks based saliency maps are generated by using the mentioned features. These saliency maps are used to create a final saliency map. The applicability, effectiveness and consistency of the proposed approach are illustrated by conducting some experiments using publicly available datasets. The tests have been used to compare the proposed method with some state-of-the-art methods. The reported results cover qualitative and quantitative assessments which demonstrate that our approach outputs high quality saliency maps and mostly achieves the highest precision rate compared to the other methods.
URI: https://www.sciencedirect.com/science/article/pii/S0031320317300110?via%3Dihub
https://hdl.handle.net/20.500.11851/1176
ISSN: 0031-3203
Appears in Collections:Bilgisayar Mühendisliği Bölümü / Department of Computer Engineering
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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