Optimizing the relevance-redundancy tradeoff for efficient semantic segmentation
Hazırbaş C, Diebold J, Cremers D (2015)
Publication Type: Conference contribution
Publication year: 2015
Journal
Publisher: Springer Verlag
Book Volume: 9087
Pages Range: 243-255
Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Event location: Lege-Cap Ferret, FRA
ISBN: 9783319184609
DOI: 10.1007/978-3-319-18461-6_20
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.
Involved external institutions
Germany (DE)How to cite
APA:
Hazırbaş, C., Diebold, J., & Cremers, D. (2015). Optimizing the relevance-redundancy tradeoff for efficient semantic segmentation. In Mila Nikolova, Jean-François Aujol, Nicolas Papadakis (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 243-255). Lege-Cap Ferret, FRA: Springer Verlag.
MLA:
Hazırbaş, Caner, Julia Diebold, and Daniel Cremers. "Optimizing the relevance-redundancy tradeoff for efficient semantic segmentation." Proceedings of the 5th International Conference on Scale Space and Variational Methods in Computer Vision, SSVM 2015, Lege-Cap Ferret, FRA Ed. Mila Nikolova, Jean-François Aujol, Nicolas Papadakis, Springer Verlag, 2015. 243-255.
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