University of Oulu

Melekhov I., Kannala J., Rahtu E. (2017) Image Patch Matching Using Convolutional Descriptors with Euclidean Distance. In: Chen CS., Lu J., Ma KK. (eds) Computer Vision – ACCV 2016 Workshops. ACCV 2016. Lecture Notes in Computer Science, vol 10118. Springer, Cham

Image patch matching using convolutional descriptors with euclidean distance

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Author: Melekhov, Iaroslav1; Kannala, Juho1; Rahtu, Esa2
Organizations: 1Department of Computer Science, Aalto University, Espoo, Finland
2Center for Machine Vision Research, University of Oulu, Oulu, Finland
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 2.5 MB)
Persistent link: http://urn.fi/urn:nbn:fi-fe2019060518462
Language: English
Published: Springer Nature, 2017
Publish Date: 2019-06-05
Description:

Abstract

In this work we propose a neural network based image descriptor suitable for image patch matching, which is an important task in many computer vision applications. Our approach is influenced by recent success of deep convolutional neural networks (CNNs) in object detection and classification tasks. We develop a model which maps the raw input patch to a low dimensional feature vector so that the distance between representations is small for similar patches and large otherwise. As a distance metric we utilize L₂ norm, i.e. Euclidean distance, which is fast to evaluate and used in most popular hand-crafted descriptors, such as SIFT. According to the results, our approach outperforms state-of-the-art L₂-based descriptors and can be considered as a direct replacement of SIFT. In addition, we conducted experiments with batch normalization and histogram equalization as a preprocessing method of the input data. The results confirm that these techniques further improve the performance of the proposed descriptor. Finally, we show promising preliminary results by appending our CNNs with recently proposed spatial transformer networks and provide a visualisation and interpretation of their impact.

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Series: Lecture notes in computer science
ISSN: 0302-9743
ISSN-E: 1611-3349
ISSN-L: 0302-9743
ISBN: 978-3-319-54526-4
ISBN Print: 978-3-319-54525-7
Pages: 638 - 653
DOI: 10.1007/978-3-319-54526-4_46
OADOI: https://oadoi.org/10.1007/978-3-319-54526-4_46
Host publication: Computer Vision – ACCV 2016 Workshops. ACCV 2016. ACCV 2016 International Workshops, Taipei, Taiwan, November 20-24, 2016, Revised Selected Papers, Part III
Host publication editor: Chen, Chu-Song
Lu, Jiwen
Ma, Kai-Kuang
Conference: Asian Conference on Computer Vision
Type of Publication: A4 Article in conference proceedings
Field of Science: 213 Electronic, automation and communications engineering, electronics
Subjects:
Copyright information: © Springer International Publishing AG 2017. This is a post-peer-review, pre-copyedit version of an article published in ACCV 2016: Computer Vision – ACCV 2016 Workshops. The final authenticated version is available online at: https://doi.org/10.1007/978-3-319-54526-4_46.