MVFFNet: Multi-view feature fusion network for imbalanced ship classification

Liang, Maohan; Yang, Zhan; Liu, Ryan Wen

doi:10.1016/j.patrec.2021.07.024

Cited by 29 publications

(10 citation statements)

References 28 publications

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“…As well as descriptors relating to speed and distance, and course, that article benefited from an array of descriptors that captured the turn features of the vessels. The authors of [ 15 ] proposed a feature fusion network (among several others) to achieve accurate multiclassification, with an emphasis on handling imbalanced data. Eight types of ships were investigated in that article, including cargo ships, passenger ships, oil tankers, towing ships, container ships, pilot ships, law enforcement ships, and fishing ships.…”

Section: Introductionmentioning

confidence: 99%

A Study on the Geometric and Kinematic Descriptors of Trajectories in the Classification of Ship Types

Tavakoli

Peña‐Castillo

Soares

2022

Sensors

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The classification of ships based on their trajectory descriptors is a common practice that is helpful in various contexts, such as maritime security and traffic management. For the most part, the descriptors are either geometric, which capture the shape of a ship’s trajectory, or kinematic, which capture the motion properties of a ship’s movement. Understanding the implications of the type of descriptor that is used in classification is important for feature engineering and model interpretation. However, this matter has not yet been deeply studied. This article contributes to feature engineering within this field by introducing proper similarity measures between the descriptors and defining sound benchmark classifiers, based on which we compared the predictive performance of geometric and kinematic descriptors. The performance profiles of geometric and kinematic descriptors, along with several standard tools in interpretable machine learning, helped us to provide an account of how different ships differ in movement. Our results indicated that the predictive performance of geometric and kinematic descriptors varied greatly, depending on the classification problem at hand. We also showed that the movement of certain ship classes solely differed geometrically while some other classes differed kinematically and that this difference could be formulated in simple terms. On the other hand, the movement characteristics of some other ship classes could not be delineated along these lines and were more complicated to express. Finally, this study verified the conjecture that the geometric–kinematic taxonomy could be further developed as a tool for more accessible feature selection.

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Section: Introductionmentioning

confidence: 99%

A Study on the Geometric and Kinematic Descriptors of Trajectories in the Classification of Ship Types

Tavakoli

Peña‐Castillo

Soares

2022

Sensors

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“…Firstly, we use part of the video frame data to train the network model. en, we use the trained model to reduce the dimension of all the video frames and perform multiview fusion for low-dimensional features [15]. Finally, we use the fused features to extract key frames.…”

Section: Introductionmentioning

confidence: 99%

A Video Key Frame Extraction Method Based on Multiview Fusion

Chen¹,

Wang²,

Yang³

et al. 2022

Mobile Information Systems

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A massive amount of video data is stored in the real-time road monitoring system, especially in high-speed scenes. Traditional methods of video key frame extraction have the problems of large computation and long-time consumption. Thus, it is imperative to decrease the massive video data generated by monitoring and help researchers to study key frames. Aiming at the above problems, we propose an efficient key frame extraction method based on multiview fusion, where the autoencoder is used to compress the video data. Specifically, all the video frames of the video data are subjected to feature dimensionality reduction, and the features after dimensionality reduction are subjected to multiview fusion. Finally, dynamic programming and clustering are used to extract key frames. The experimental results show that the proposed method has lower computational complexity in extracting key frames, while the mutual information in the extracted key frames is large. It illustrates the reliability and efficiency of the proposed method, which provides technical support for subsequent video research.

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“…Such applications are in fact permeate various fields of study ([6]). One for example may mention, recognition of ship types and their activities ( [7], [8], [9], [10], [11], [12], [13], [14], [15], [16], and [17]), transportation modes ( [18], [19], [20], [21], [22], [23], [24], [25], and [26]), recognition of animals and their behaviour ( [5], [27], [28], [29], [30] [10], and [21]), labeling hurricanes ( [31] and [21]), recognition of flying objects ( [32] and [33]), gesture recognition ( [34]), and labeling abnormal movements ( [35]).…”

Section: Introductionmentioning

confidence: 99%

Classification of Moving Objects Under theSpatial-Spatiotemporal Taxonomy of Descriptor

Tavakoli¹

2022

Proceedings of the Canadian Conference on Artificial Intelligence

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MVFFNet: Multi-view feature fusion network for imbalanced ship classification

Cited by 29 publications

References 28 publications

A Study on the Geometric and Kinematic Descriptors of Trajectories in the Classification of Ship Types

A Study on the Geometric and Kinematic Descriptors of Trajectories in the Classification of Ship Types

A Video Key Frame Extraction Method Based on Multiview Fusion

Classification of Moving Objects Under theSpatial-Spatiotemporal Taxonomy of Descriptor

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