Affective image classification via semi-supervised learning from web images

Affective image classification via semi-supervised learning from web images Multimed Tools Appl https://doi.org/10.1007/s11042-018-6131-1 Affective image classification via semi-supervised learning from web images 1 1,2 Na Li · Yong Xia Received: 31 October 2017 / Revised: 19 April 2018 / Accepted: 9 May 2018 © Springer Science+Business Media, LLC, part of Springer Nature 2018 Abstract Affective image classification has drawn increasing research attentions in the affective computing and multimedia communities. Despite many solutions proposed in the literature, it remains a major challenge to bridge the semantic gap between visual features of images and their affective characteristics, partly due to the lack of adequate training samples, which can be largely ascribed to the all-consuming nature of affective image anno- tation. In this paper, we propose a novel affective image classification algorithm based on semi-supervised learning from web images (SSL-WI). This algorithm consists of four major steps, including color and texture feature extraction, baseline classifier construction, feature selection, and jointly using training images and retrieved web images to re-train the classi- fier. We have applied this algorithm, the baseline classifier that is not trained by web images, and two state-of-the-art algorithms to differentiating color images in a three-dimensional discrete emotional space. Our results suggest that, with the scheme of semi-supervised learning http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Multimedia Tools and Applications Springer Journals

Affective image classification via semi-supervised learning from web images

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Publisher
Springer US
Copyright
Copyright © 2018 by Springer Science+Business Media, LLC, part of Springer Nature
Subject
Computer Science; Multimedia Information Systems; Computer Communication Networks; Data Structures, Cryptology and Information Theory; Special Purpose and Application-Based Systems
ISSN
1380-7501
eISSN
1573-7721
D.O.I.
10.1007/s11042-018-6131-1
Publisher site
See Article on Publisher Site

Abstract

Multimed Tools Appl https://doi.org/10.1007/s11042-018-6131-1 Affective image classification via semi-supervised learning from web images 1 1,2 Na Li · Yong Xia Received: 31 October 2017 / Revised: 19 April 2018 / Accepted: 9 May 2018 © Springer Science+Business Media, LLC, part of Springer Nature 2018 Abstract Affective image classification has drawn increasing research attentions in the affective computing and multimedia communities. Despite many solutions proposed in the literature, it remains a major challenge to bridge the semantic gap between visual features of images and their affective characteristics, partly due to the lack of adequate training samples, which can be largely ascribed to the all-consuming nature of affective image anno- tation. In this paper, we propose a novel affective image classification algorithm based on semi-supervised learning from web images (SSL-WI). This algorithm consists of four major steps, including color and texture feature extraction, baseline classifier construction, feature selection, and jointly using training images and retrieved web images to re-train the classi- fier. We have applied this algorithm, the baseline classifier that is not trained by web images, and two state-of-the-art algorithms to differentiating color images in a three-dimensional discrete emotional space. Our results suggest that, with the scheme of semi-supervised learning

Journal

Multimedia Tools and Applications – Springer Journals

Published: May 31, 2018

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