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2015 | OriginalPaper | Buchkapitel

\(N^4\)-Fields: Neural Network Nearest Neighbor Fields for Image Transforms

verfasst von : Yaroslav Ganin, Victor Lempitsky

Erschienen in: Computer Vision -- ACCV 2014

Verlag: Springer International Publishing

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Abstract

We propose a new architecture for difficult image processing operations, such as natural edge detection or thin object segmentation. The architecture is based on a simple combination of convolutional neural networks with the nearest neighbor search.

We focus our attention on the situations when the desired image transformation is too hard for a neural network to learn explicitly. We show that in such situations the use of the nearest neighbor search on top of the network output allows to improve the results considerably and to account for the underfitting effect during the neural network training. The approach is validated on three challenging benchmarks, where the performance of the proposed architecture matches or exceeds the state-of-the-art.

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Vorheriges Kapitel Saliency Detection via Nonlocal Minimization

Nächstes Kapitel Super-Resolution Using Sub-Band Self-Similarity

https://code.google.com/p/cuda-convnet/.

http://sites.skoltech.ru/compvision/projects/n4/ at the moment of publication.

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Titel: -Fields: Neural Network Nearest Neighbor Fields for Image Transforms
verfasst von: Yaroslav Ganin
Victor Lempitsky
Verlag: Springer International Publishing
Buch: Computer Vision -- ACCV 2014
Print ISBN: 978-3-319-16807-4

Electronic ISBN: 978-3-319-16808-1

Copyright-Jahr: 2015
DOI: https://doi.org/10.1007/978-3-319-16808-1_36

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