@inproceedings{9c4dfb1c9b1d408a948bf852712292e2,
title = "Optimizing the relevance-redundancy tradeoff for efficient semantic segmentation",
abstract = "Semantic segmentation aims at jointly computing a segmentation and a semantic labeling of the image plane. The main ingredient is an efficient feature selection strategy. In this work we perform a systematic information-theoretic evaluation of existing features in order to address the question which and how many features are appropriate for an efficient semantic segmentation. To this end, we discuss the tradeoff between relevance and redundancy and present an information-theoretic feature evaluation strategy. Subsequently, we perform a systematic experimental validation which shows that the proposed feature selection strategy provides state-of-the-art semantic segmentations on five semantic segmentation datasets at significantly reduced runtimes. Moreover, it provides a systematic overview of which features are the most relevant for various benchmarks.",
keywords = "Feature analysis, Feature selection, Image segmentation, Semantic scene understanding",
author = "Caner Hazırba{\c s} and Julia Diebold and Daniel Cremers",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2015.; 5th International Conference on Scale Space and Variational Methods in Computer Vision, SSVM 2015 ; Conference date: 31-05-2015 Through 04-06-2015",
year = "2015",
doi = "10.1007/978-3-319-18461-6_20",
language = "English",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "243--255",
editor = "Mila Nikolova and Jean-Fran{\c c}ois Aujol and Nicolas Papadakis",
booktitle = "Scale Space and Variational Methods in Computer Vision - 5th International Conference, SSVM 2015, Proceedings",
}