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Erschienen in:
Hybrid Example-Based Single Image Super-Resolution Kapitel lesen Erstes Kapitel lesen

2015 | OriginalPaper | Buchkapitel

WHAT2PRINT: Learning Image Evaluation

verfasst von : Bohao She, Clark F. Olson

Erschienen in: Advances in Visual Computing

Verlag: Springer International Publishing

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Abstract

The popularity of digital photography has changed the way images that are taken, processed, and stored. This has created a demand for systems that can evaluate the aesthetic quality of images. Applications that auto-assess image aesthetic quality and modify images to raise their aesthetic quality are widely available, but applications that automatically select aesthetic images from a given image collection are limited. The goal of this project is to create a portable application that can recommend user-given images from a given image collection, using criteria learned from user preferences. We train a Support Vector Machine on seven extracted image features. This system achieves a correct prediction rate of 70 % on a public image dataset. The use of additional or improved features should yield increased prediction rates.

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Vorheriges Kapitel Home Oriented Virtual e-Rehabilitation
Nächstes Kapitel Use of a Large Image Repository to Enhance Domain Dataset for Flyer Classification
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Metadaten
Titel
WHAT2PRINT: Learning Image Evaluation
verfasst von
Bohao She
Clark F. Olson
Copyright-Jahr
2015
Buch
Advances in Visual Computing

Print ISBN: 978-3-319-27862-9

Electronic ISBN: 978-3-319-27863-6

Copyright-Jahr: 2015

DOI
https://doi.org/10.1007/978-3-319-27863-6_55

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