TY - GEN
T1 - MARVEL
T2 - 13th Asian Conference on Computer Vision, ACCV 2016
AU - Gundogdu, Erhan
AU - Solmaz, Berkan
AU - Yücesoy, Veysel
AU - Koç, Aykut
N1 - Publisher Copyright:
© Springer International Publishing AG 2017.
PY - 2017
Y1 - 2017
N2 - Fine-grained visual categorization has recently received great attention as the volumes of the labelled datasets for classification of specific objects, such as cars, bird species, and aircrafts, have been increasing. The collection of large datasets has helped vision based classification approaches and led to significant improvements in performances of the state-of-the-art methods. Visual classification of maritime vessels is another important task assisting naval security and surveillance applications. In this work, we introduce a large-scale image dataset for maritime vessels, consisting of 2 million user uploaded images and their attributes including vessel identity, type, photograph category and year of built, collected from a community website. We categorize the images into 109 vessel type classes and construct 26 superclasses by combining heavily populated classes with a semi-automatic clustering scheme. For the analysis of our dataset, extensive experiments have been performed, involving four potentially useful applications; vessel classification, verification, retrieval, and recognition. We report encouraging results for each application. The introduced dataset is publicly available.
AB - Fine-grained visual categorization has recently received great attention as the volumes of the labelled datasets for classification of specific objects, such as cars, bird species, and aircrafts, have been increasing. The collection of large datasets has helped vision based classification approaches and led to significant improvements in performances of the state-of-the-art methods. Visual classification of maritime vessels is another important task assisting naval security and surveillance applications. In this work, we introduce a large-scale image dataset for maritime vessels, consisting of 2 million user uploaded images and their attributes including vessel identity, type, photograph category and year of built, collected from a community website. We categorize the images into 109 vessel type classes and construct 26 superclasses by combining heavily populated classes with a semi-automatic clustering scheme. For the analysis of our dataset, extensive experiments have been performed, involving four potentially useful applications; vessel classification, verification, retrieval, and recognition. We report encouraging results for each application. The introduced dataset is publicly available.
UR - http://www.scopus.com/inward/record.url?scp=85016275593&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85016275593&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-54193-8_11
DO - 10.1007/978-3-319-54193-8_11
M3 - Conference contribution
AN - SCOPUS:85016275593
SN - 9783319541921
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 165
EP - 180
BT - Computer Vision - 13th Asian Conference on Computer Vision, ACCV 2016, Revised Selected Papers
A2 - Nishino, Ko
A2 - Lai, Shang-Hong
A2 - Lepetit, Vincent
A2 - Sato, Yoichi
PB - Springer Verlag
Y2 - 20 November 2016 through 24 November 2016
ER -