Autoencoder-based image processing framework for object appearance modifications

The presented paper introduces a novel method for enabling appearance modifications for complex image objects. Qualitative visual object properties, quantified using appropriately derived visual attribute descriptors, are subject to alterations. We adopt a basic convolutional autoencoder as a framew...

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Bibliographic Details
Published in:Neural computing & applications Vol. 33; no. 4; pp. 1079 - 1090
Main Authors: Ślot, Krzysztof, Kapusta, Paweł, Kucharski, Jacek
Format: Journal Article
Language:English
Published: London Springer London 01.02.2021
Springer Nature B.V
Subjects:
Algorithms
Artificial Intelligence
Aspect ratio
Computational Biology/Bioinformatics
Computational Science and Engineering
Computer Science
Data Mining and Knowledge Discovery
Datasets
Handwriting
Image processing
Image Processing and Computer Vision
Image reconstruction
Original Article
Principal components analysis
Probability and Statistics in Computer Science
Representations
Convolutional autoencoders
Supervised principal component analysis
Visual object editing
Machine learning
ISSN:0941-0643, 1433-3058
Online Access:Get full text
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Summary:The presented paper introduces a novel method for enabling appearance modifications for complex image objects. Qualitative visual object properties, quantified using appropriately derived visual attribute descriptors, are subject to alterations. We adopt a basic convolutional autoencoder as a framework for the proposed attribute modification algorithm, which is composed of the following three steps. The algorithm begins with the extraction of attribute-related information from autoencoder’s latent representation of an input image, by means of supervised principal component analysis. Next, appearance alteration is performed in the derived feature space (referred to as ‘attribute-space’), based on appropriately identified mappings between quantitative descriptors of image attributes and attribute-space features. Finally, modified attribute vectors are transformed back to latent representation, and output image is reconstructed in the decoding part of an autoencoder. The method has been evaluated using two datasets: images of simple objects—digits from MNIST handwritten-digit dataset and images of complex objects—faces from CelebA dataset. In the former case, two qualitative visual attributes of digit images have been selected for modifications: slant and aspect ratio, whereas in the latter case, aspect ratio of face oval was subject to alterations. Evaluation results prove, both in qualitative and quantitative terms, that the proposed framework offers a promising tool for visual object editing.
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ISSN:0941-0643
1433-3058
DOI:10.1007/s00521-020-04976-7