Converting Image Labels to Meaningful and Information-rich Embeddings

Savvas Karatsiolis; Andreas Kamilaris

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Paper

Converting Image Labels to Meaningful and Information-rich Embeddings

Topics: Deep Learning and Neural Networks; Feature Selection and Extraction; Image and Video Analysis and Understanding; Machine Learning Methods

In Proceedings of the 10th International Conference on Pattern Recognition Applications and Methods ICPRAM - Volume 1, 107-119, 2021

Authors: Savvas Karatsiolis and Andreas Kamilaris

Affiliation: Research Centre on Interactive Media, Smart Systems and Emerging Technologies (RISE), Nicosia, Cyprus

Keyword(s): Convolutional Neural Networks, Disentangled Latent Space, Representation Learning, Siamese Network.

Abstract: A challenge of the computer vision community is to understand the semantics of an image that will allow for higher quality image generation based on existing high-level features and better analysis of (semi-) labeled datasets. Categorical labels aggregate a huge amount of information into a binary value which conceals valuable high-level concepts from the Machine Learning models. Towards addressing this challenge, this paper introduces a method, called Occlusion-based Latent Representations (OLR), for converting image labels to meaningful representations that capture a significant amount of data semantics. Besides being information-rich, these representations compose a disentangled low-dimensional latent space where each image label is encoded into a separate vector. We evaluate the quality of these representations in a series of experiments whose results suggest that the proposed model can capture data concepts and discover data interrelations.

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Paper citation in several formats:

Karatsiolis, S. and Kamilaris, A. (2021). Converting Image Labels to Meaningful and Information-rich Embeddings. In Proceedings of the 10th International Conference on Pattern Recognition Applications and Methods - ICPRAM; ISBN 978-989-758-486-2; ISSN 2184-4313, SciTePress, pages 107-119. DOI: 10.5220/0010375801070119

@conference{icpram21,
author={Savvas Karatsiolis and Andreas Kamilaris},
title={Converting Image Labels to Meaningful and Information-rich Embeddings},
booktitle={Proceedings of the 10th International Conference on Pattern Recognition Applications and Methods - ICPRAM},
year={2021},
pages={107-119},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010375801070119},
isbn={978-989-758-486-2},
issn={2184-4313},
}

TY - CONF

JO - Proceedings of the 10th International Conference on Pattern Recognition Applications and Methods - ICPRAM
TI - Converting Image Labels to Meaningful and Information-rich Embeddings
SN - 978-989-758-486-2
IS - 2184-4313
AU - Karatsiolis, S.
AU - Kamilaris, A.
PY - 2021
SP - 107
EP - 119
DO - 10.5220/0010375801070119
PB - SciTePress