VESSELS.bib

@InProceedings{10.1007/978-3-319-54193-8_11,
author="Gundogdu, Erhan
and Solmaz, Berkan
and Y{\"u}cesoy, Veysel
and Ko{\c{c}}, Aykut",
editor="Lai, Shang-Hong
and Lepetit, Vincent
and Nishino, Ko
and Sato, Yoichi",
title="MARVEL: A Large-Scale Image Dataset for Maritime Vessels",
booktitle="Computer Vision --  ACCV 2016",
year="2017",
publisher="Springer International Publishing",
address="Cham",
pages="165--180",
abstract="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.",
isbn="978-3-319-54193-8"
}


@Article{Solmaz2017,
author="Solmaz, Berkan
and Gundogdu, Erhan
and Yucesoy, Veysel
and Koc, Aykut",
title="Generic and attribute-specific deep representations for maritime vessels",
journal="IPSJ Transactions on Computer Vision and Applications",
year="2017",
month="Dec",
day="11",
volume="9",
number="1",
pages="22",
abstract="Fine-grained visual categorization has recently received great attention as the volumes of labeled datasets for classification of specific objects, such as cars, bird species, and air-crafts, have been increasing. The availability of large datasets led to significant performance improvements in several vision-based classification tasks. Visual classification of maritime vessels is another important task, assisting naval security and surveillance applications. We introduced, MARVEL, a large-scale image dataset for maritime vessels, consisting of 2 million user-uploaded images and their various attributes, including vessel identity, type, category, year built, length, and tonnage, collected from a community website. The images were categorized into vessel type classes and also into superclasses defined by combining semantically similar classes, following a semi-automatic clustering scheme. For the analysis of the presented dataset, extensive experiments have been performed, involving several potentially useful applications: vessel type classification, identity verification, retrieval, and identity recognition with and without prior vessel type knowledge. Furthermore, we attempted interesting problems of visual marine surveillance such as predicting and classifying maritime vessel attributes such as length, summer deadweight, draught, and gross tonnage by solely interpreting the visual content in the wild, where no additional cues such as scale, orientation, or location are provided. By utilizing generic and attribute-specific deep representations for maritime vessels, we obtained promising results for the aforementioned applications.",
issn="1882-6695",
doi="10.1186/s41074-017-0033-4",
url="https://doi.org/10.1186/s41074-017-0033-4"
}

@ARTICLE{iet:/content/journals/10.1049/iet-cvi.2018.5187,
   author = {Berkan Solmaz},
   author = {Erhan Gundogdu},
   author = {Veysel Yucesoy},
   author = {Aykut Koç},
   author = {Abdullah Aydin Alatan},
   keywords = {large-scale video analysis;deep feature embedding;hierarchical individual sample label;visual recognition;data pairs;fine-grained maritime vessel recognition;verification task;deep learning-based approaches;fine-grained classification task;fine-grained land vehicle recognition;maritime vessel identification;fine-grained retrieval task;land vehicle classification;loss function;coarse-grained retrieval task;Stanford Cars data set;large-scale image analysis;MARVEL data set;land vehicle identification;fine-grained object recognition;computer vision problems;multitask learning framework;visual surveillance systems;global statistics;maritime vessel classification;coarse-grained classification task;},
   ISSN = {1751-9632},
   language = {English},
   abstract = {Recent advances in large-scale image and video analysis have empowered the potential capabilities of visual surveillance systems. In particular, deep learning-based approaches bring in substantial benefits in solving certain computer vision problems such as fine-grained object recognition. Here, the authors mainly concentrate on classification and identification of maritime vessels and land vehicles, which are the key constituents of visual surveillance systems. Employing publicly available data sets for maritime vessels and land vehicles, the authors aim to improve visual recognition. Specifically, the authors focus on five tasks regarding visual recognition; coarse-grained classification, fine-grained classification, coarse-grained retrieval, fine-grained retrieval, and verification. To increase the performance in these tasks, the authors utilise a multi-task learning framework and present a novel loss function which simultaneously considers deep feature learning and classification by exploiting the available hierarchical labels of individual samples and the global statistics of distances between the data pairs. The authors observe that the proposed multi-task learning model improves the fine-grained recognition performance on MARVEL and Stanford Cars data sets, compared to training of a model targeting a single recognition task.},
   title = {Fine-grained recognition of maritime vessels and land vehicles by deep feature embedding},
   journal = {IET Computer Vision},
   year = {2018},
   month = {July},
   publisher ={Institution of Engineering and Technology},
   copyright = {© The Institution of Engineering and Technology},
   url = {http://digital-library.theiet.org/content/journals/10.1049/iet-cvi.2018.5187}
}