Deep distance metric learning for maritime vessel identification

Gundogdu, Erhan; Solmaz, Berkan; Koç, Aykut; Yücesoy, Veysel; Alatan, A. Aydın

doi:10.1109/siu.2017.7960170

Cited by 7 publications

(9 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Most of the existing deep learning approaches are based on the deep architectural background rather than the distance metric in a new representation space of the data. However, distance-based approaches have recently become one of the most interesting topics in deep learning [15,[23][24][25][26]. While decreasing the distance between dissimilar samples [27,28], deep metric learning, which aims to increase the distance between similar samples, is directly related to the distance between samples.…”

Section: Deep Metric Learningmentioning

confidence: 99%

Deep Metric Learning: A Survey

2019

View full text Add to dashboard Cite

Metric learning aims to measure the similarity among samples while using an optimal distance metric for learning tasks. Metric learning methods, which generally use a linear projection, are limited in solving real-world problems demonstrating non-linear characteristics. Kernel approaches are utilized in metric learning to address this problem. In recent years, deep metric learning, which provides a better solution for nonlinear data through activation functions, has attracted researchers' attention in many different areas. This article aims to reveal the importance of deep metric learning and the problems dealt with in this field in the light of recent studies. As far as the research conducted in this field are concerned, most existing studies that are inspired by Siamese and Triplet networks are commonly used to correlate among samples while using shared weights in deep metric learning. The success of these networks is based on their capacity to understand the similarity relationship among samples. Moreover, sampling strategy, appropriate distance metric, and the structure of the network are the challenging factors for researchers to improve the performance of the network model. This article is considered to be important, as it is the first comprehensive study in which these factors are systematically analyzed and evaluated as a whole and supported by comparing the quantitative results of the methods.

show abstract

Section: Deep Metric Learningmentioning

confidence: 99%

Deep Metric Learning: A Survey

2019

View full text Add to dashboard Cite

show abstract

“…We compare the performance of our proposed method against the state-of-the-art feature learning approaches in [18,21,4,22,20] by using the same evaluation methods. In addition, the randomly selected quadruplets are utilized as in [9]. Stanford Cars 196 dataset [7] is used in the experiments.…”

Section: Resultsmentioning

confidence: 99%

“…C2 Fig. 1: The proposed framework is similar to the model used in [9]. The dimension of the last fully connected (FC) layer is 1024.…”

Section: Distance Cost Functionmentioning

confidence: 99%

“…Moreover, the loss function for the quadruplets is similar to the triplet based methods [6]. On the other hand, in [9], the use of the global loss has been proposed, while the quadruplet samples are selected randomly (Note that these quadruplets hold the constraints). The global loss penalizes the network in case of the mean and variance of the distances between the samples in a quadruplet are not appropriate, as given in (3) 5 , where t 1 and t 2 are the margins, similar to (2).…”

Section: Related Workmentioning

confidence: 99%

“…Some of these datasets can be used for classifying land, marine, and air vehicles in a real-world scenario. Concretely, car model recognition can be employed in the context of visual surveillance and security for the land traffic control [6] and marine vessel recognition is used for the purpose of coastal surveillance [9] [10]. In this work, we focus on the feature learning problem specifically designed for car model recognition.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Quadruplet Selection Methods for Deep Embedding Learning

Karaman

Gundogdu

Koç

et al. 2019

2019 IEEE International Conference on Image Processing (ICIP)

Self Cite

View full text Add to dashboard Cite

Recognition of objects with subtle differences has been used in many practical applications, such as car model recognition and maritime vessel identification. For discrimination of the objects in fine-grained detail, we focus on deep embedding learning by using a multi-task learning framework, in which the hierarchical labels (coarse and fine labels) of the samples are utilized both for classification and a quadruplet-based loss function. In order to improve the recognition strength of the learned features, we present a novel feature selection method specifically designed for four training samples of a quadruplet. By experiments, it is observed that the selection of very hard negative samples with relatively easy positive ones from the same coarse and fine classes significantly increases some performance metrics in a fine-grained dataset when compared to selecting the quadruplet samples randomly. The feature embedding learned by the proposed method achieves favorable performance against its state-of-the-art counterparts.

show abstract

Fine‐grained recognition of maritime vessels and land vehicles by deep feature embedding

Solmaz

Gundogdu

Yücesoy

et al. 2018

IET Computer Vision

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Deep distance metric learning for maritime vessel identification

Cited by 7 publications

References 8 publications

Deep Metric Learning: A Survey

Deep Metric Learning: A Survey

Quadruplet Selection Methods for Deep Embedding Learning

Fine‐grained recognition of maritime vessels and land vehicles by deep feature embedding

Contact Info

Product

Resources

About