Abstract
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.
Highlights
The advent of deep neural networks and deep learning methodologies was a breakthrough in artificial intelligence, enabling its applications in a wide variety of engineering tasks
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
We propose to apply supervised principal component analysis [3], which attempts to appropriately combine pieces of attribute-related information, scattered among latent vector components, into features that strongly correlate with target attributes
Summary
The advent of deep neural networks and deep learning methodologies was a breakthrough in artificial intelligence, enabling its applications in a wide variety of engineering tasks. We propose a data processing scheme that is different from the existing paradigms: Contents related to a specific appearance attribute are extracted from latent space through transformation, which decorrelates it from information on other high-level visual properties, so that it can be selectively manipulated without affecting the remaining components. This contrasts with other approaches, which apply direct transformations to latent space vectors, and require additional mechanisms for disentangling existing between-attribute dependencies.
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