Weight estimation in hypergraph learning
Konstantinos Pliakos, Constantine Kotropoulos
Abstract
The unremitting rising popularity of social media has led to an exponential increase in web activity as manifested by the vast volume of uploaded images. This boundless volume of image data has triggered the interest in image tagging. Here, an efficient hypergraph weight estimation scheme is proposed that improves the accuracy of image tagging, using hypergraph learning. The proposed method models high-order relations between hypergraph vertices (i.e., users, user social groups, tags, geo-tags, and images) by hyperedges. The information captured by the hyperedges is efficiently distilled by estimating the hyperedge weights. Experiments conducted on a dataset crawled from Flickr demonstrate the effectiveness of the proposed approach. Specifically, an average precision of 91% at 26% recall has been achieved for image tagging.
BibTeX
@inproceedings{icassp2015_weightestimation,
title = {Weight estimation in hypergraph learning},
author = {Konstantinos Pliakos and Constantine Kotropoulos},
booktitle = {ICASSP 2015},
year = {2015}
}