Image patch matching using convolutional descriptors with Euclidean distance

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

4 Citations (Scopus)

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.
Original languageEnglish
Title of host publicationComputer Vision - ACCV 2016 Workshops, ACCV 2016 International Workshops, Revised Selected Papers
Pages638-653
Number of pages16
DOIs
Publication statusPublished - 2017
MoE publication typeA4 Article in a conference publication
EventAsian Conference on Computer Vision - Taipei, Taiwan, Republic of China
Duration: 20 Nov 201624 Nov 2016
Conference number: 13

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10118 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferenceAsian Conference on Computer Vision
Abbreviated titleACCV
CountryTaiwan, Republic of China
CityTaipei
Period20/11/201624/11/2016

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  • Cite this

    Melekhov, I., Kannala, J., & Rahtu, E. (2017). Image patch matching using convolutional descriptors with Euclidean distance. In Computer Vision - ACCV 2016 Workshops, ACCV 2016 International Workshops, Revised Selected Papers (pp. 638-653). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10118 LNCS). https://doi.org/10.1007/978-3-319-54526-4_46