NDLI: Scene segmentation and labeling using multi-hypothesis superpixels

Content Provider	IEEE Xplore Digital Library
Author	Ak, K.E. Ates, H.F.
Copyright Year	2015
Description	Author affiliation: Elektrik-Elektron. Muhendisligi Bolumu, Isik Univ., İstanbul, Turkey (Ak, K.E.; Ates, H.F.)
Abstract	Superpixels recently gained in importance in image segmentation and classification problems. In scene labeling the image is initially segmented into visually consistent small regions using a superpixel algorithm; then, superpixels are parsed into different classes. Classification performance heavily depends on the properties and parametric settings of the superpixel algorithm in use. In this paper, a method is proposed to improve scene labeling accuracy by fusing at classifier level the results of multiple superpixel segmentations. First, likelihood ratios are determined for superpixel labels using simple, nonparametric SuperParsing algorithm, which requires no training. Then, final scene segmentation and labeling is performed by pixel-level fusion of the likelihood ratios that are computed for alternative superpixel segmentation scenarios. The proposed method is tested on the SIFT Flow dataset consisting of 2,688 images and 33 labels, and is shown to outperform SuperParsing in terms of classification accuracy.
Starting Page	847
Ending Page	850
File Size	309646
Page Count	4
File Format	PDF
ISBN	9781467373869
DOI	10.1109/SIU.2015.7129961
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-05-16
Publisher Place	Turkey
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Image segmentation Computer vision Reactive power Histograms Image recognition Accuracy image segmentation superpixel Labeling image parsing
Content Type	Text
Resource Type	Article

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