Image patch matching using convolutional descriptors with Euclidean distance

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussaConference contributionScientificvertaisarvioitu

4 Sitaatiot (Scopus)

Abstrakti

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.
AlkuperäiskieliEnglanti
OtsikkoComputer Vision - ACCV 2016 Workshops, ACCV 2016 International Workshops, Revised Selected Papers
Sivut638-653
Sivumäärä16
DOI - pysyväislinkit
TilaJulkaistu - 2017
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisuussa
TapahtumaAsian Conference on Computer Vision - Taipei, Taiwan
Kesto: 20 marraskuuta 201624 marraskuuta 2016
Konferenssinumero: 13

Julkaisusarja

NimiLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Vuosikerta10118 LNCS
ISSN (painettu)0302-9743
ISSN (elektroninen)1611-3349

Conference

ConferenceAsian Conference on Computer Vision
LyhennettäACCV
MaaTaiwan
KaupunkiTaipei
Ajanjakso20/11/201624/11/2016

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  • Siteeraa tätä

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