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arXiv · 2008.00665

The pursuit of beauty: Converting image labels to meaningful vectors

Savvas Karatsiolis·Andreas Kamilaris
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Abstract

A challenge of the computer vision community is to understand the semantics of an image, in order to allow image reconstruction based on existing high-level features or to better analyze (semi-)labelled datasets. 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 informational 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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Savvas Karatsiolis, Andreas Kamilaris. 2020-08-03. The pursuit of beauty: Converting image labels to meaningful vectors. https://arxiv.org/abs/2008.00665

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