NDLI: Semi-supervised learning based on group sparse for relative attributes

Content Provider	IEEE Xplore Digital Library
Author	Hongxue Yang Xiangwei Kong Haiyan Fu Ming Li Genping Zhao
Copyright Year	2015
Description	Author affiliation: Harbin Eng. Univ., Harbin, China (Genping Zhao) \|\| Dalian Univ. of Technol., Dalian, China (Hongxue Yang; Xiangwei Kong; Haiyan Fu; Ming Li)
Abstract	Relative attributes provide accurate information for image processing to describe which image is more natural, more open, etc. Robustness of relative attribute learning depends on the labeled comparative image pairs. However, manually labeling is a labor intensive and time-consuming task. In this paper, a semi-supervised learning approach based on group sparse is proposed to discover pairwise comparisons automatically. We generate an initial level division of the labeled training images for the basic of new constraints. Then, group sparse representation for the unlabeled images is introduced by embedding the level information into the dictionary. The semi-supervised process is conducted by selecting samples which have minimum reconstruction errors and adding new constraints to the model by comparing the selected ones with the samples in dictionary. Experiments on three public datasets demonstrate the effectiveness of our proposed method.
Starting Page	3931
Ending Page	3935
File Size	1308051
Page Count	5
File Format	PDF
e-ISBN	9781479983391
DOI	10.1109/ICIP.2015.7351542
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-09-27
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Semisupervised learning Training Dictionaries Reliability Labeling Image reconstruction Visualization semi-supervised learning Group sparse labeling relative attributes
Content Type	Text
Resource Type	Article

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