Learning to compare image patches via convolutional neural networks

In this paper we show how to learn directly from image data (i.e., without resorting to manually-designed features) a general similarity function for comparing image patches, which is a task of fundamental importance for many computer vision problems. To encode such a function, we opt for a CNN-base...

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Veröffentlicht in:2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) S. 4353 - 4361
Hauptverfasser: Zagoruyko, Sergey, Komodakis, Nikos
Format: Tagungsbericht Journal Article
Sprache:Englisch
Veröffentlicht: IEEE 01.06.2015
Schlagworte:
Adaptation models
Benchmark testing
Computational modeling
Computer architecture
Computer vision
Conferences
Convolutional codes
Mathematical analysis
Mathematical models
Neural networks
Pattern recognition
Tasks
Training
ISSN:1063-6919, 1063-6919
Online-Zugang:Volltext
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Beschreibung
Zusammenfassung:In this paper we show how to learn directly from image data (i.e., without resorting to manually-designed features) a general similarity function for comparing image patches, which is a task of fundamental importance for many computer vision problems. To encode such a function, we opt for a CNN-based model that is trained to account for a wide variety of changes in image appearance. To that end, we explore and study multiple neural network architectures, which are specifically adapted to this task. We show that such an approach can significantly outperform the state-of-the-art on several problems and benchmark datasets.
Bibliographie:ObjectType-Article-2
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ObjectType-Conference-1
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SourceType-Conference Papers & Proceedings-2
ISSN:1063-6919
1063-6919
DOI:10.1109/CVPR.2015.7299064