Converting Image Labels to Meaningful and Information-rich Embeddings

Savvas Karatsiolis, Andreas Kamilaris

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 Harvard Style

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 - Volume 1: ICPRAM, ISBN 978-989-758-486-2, pages 107-119. DOI: 10.5220/0010375801070119


in Bibtex Style

@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 - Volume 1: ICPRAM,},
year={2021},
pages={107-119},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010375801070119},
isbn={978-989-758-486-2},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 10th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Converting Image Labels to Meaningful and Information-rich Embeddings
SN - 978-989-758-486-2
AU - Karatsiolis S.
AU - Kamilaris A.
PY - 2021
SP - 107
EP - 119
DO - 10.5220/0010375801070119