Title
Which is the Better Inpainted Image?Training Data Generation Without Any Manual Operations
Abstract
This paper proposes a learning-based quality evaluation framework for inpainted results that does not require any subjectively annotated training data. Image inpainting, which removes and restores unwanted regions in images, is widely acknowledged as a task whose results are quite difficult to evaluate objectively. Thus, existing learning-based image quality assessment (IQA) methods for inpainting require subjectively annotated data for training. However, subjective annotation requires huge cost and subjects’ judgment occasionally differs from person to person in accordance with the judgment criteria. To overcome these difficulties, the proposed framework generates and uses simulated failure results of inpainted images whose subjective qualities are controlled as the training data. We also propose a masking method for generating training data towards fully automated training data generation. These approaches make it possible to successfully estimate better inpainted images, even though the task is quite subjective. To demonstrate the effectiveness of our approach, we test our algorithm with various datasets and show it outperforms existing IQA methods for inpainting.
Year
DOI
Venue
2019
10.1007/s11263-018-1132-0
International Journal of Computer Vision
Keywords
Field
DocType
Image inpainting,Image quality assessment (IQA),Learning to rank
Training set,Learning to rank,Computer vision,Annotation,Masking (art),Computer science,Image quality,Inpainting,Artificial intelligence,Machine learning
Journal
Volume
Issue
ISSN
127
11-12
1573-1405
Citations 
PageRank 
References 
2
0.36
18
Authors
6
Name
Order
Citations
PageRank
Mariko Isogawa165.86
Dan Mikami211817.60
Kosuke Takahashi355.86
daisuke iwai438344.74
Kosuke Sato538967.31
Hideaki Kimata615029.40