Ship-SIBISCaS: A First Step towards the Identification of Potential Maritime Law Infringements by means of LSA-Based Image

Abstract

Maritime safety and security are being constantly jeopardized. Therefore, identifying maritime flow irregularities (semi-)automatically may be crucial to ensure maritime security in the future. This paper presents a Ship Semantic Information-Based, Image Similarity Calculation System (Ship-SIBISCaS), which constitutes a first step towards the automatic identification of this kind of maritime irregularities. In particular, the main goal of Ship-SIBISCaS is to automatically identify the type of ship depicted in a given image (such as abandoned, cargo, container, hospital, passenger, pirate, submersible, three-decker, or warship) and, thus, classify it accordingly. This classification is achieved in Ship-SIBISCaS by finding out the similarity of the ship image and/or description with other ship images and descriptions included in its knowledge base. This similarity is calculated by means of an LSA algorithm implementation that is run on a parallel architecture consisting of CPUs and GPUs (i.e., a heterogeneous system). This implementation of the LSA algorithm has been trained with a collection of texts, extracted from Wikipedia, that associate some semantic information to ImageNet ship images. Thanks to its parallel architecture, the indexing process of this image retrieval system has been accelerated 10 times (for the 1261 ships included in ImageNet). The range of the precision of the image similarity method is 46% to 92% with 100% recall (that is, a 100% coverage of the database).

Deep Learning-based Natural Language Processing Methods Comparison for Presumptive Detection of Cyberbullying in Social Networks

“…Thus, it uses the RvNN as an alternative to the CNN clustering layer, thus reducing the loss of local information and allowing the model to efficiently capture long-term dependencies based on its tree structure. Consequently, a classification accuracy of 89.1%, as it extracts sentimental representations at word level considering syntactic and semantic relations, similar to what was achieved with techniques such as Latent semantic analysis (LSA) proposed in [22], [23].…”

A bibliometric analysis of maritime security policy: Research trends and future agenda

“…Furthermore, other authors publishing research papers related to Automatic Identification, Automation, Maritime Domain Aware, Maritime Safety, Maritime Transportation, Monitoring, Navigation, Remote Sensing, Safety, Vessel and Waterway Transportation are Kosai & Unesaki [ 63 ], G.A. León-Paredes et al [ 10 ], Stasolla et al [ 60 ], Weihai [ 62 ], and M A Zohourian [ 14 ].…”

“…Furthermore, in the shipping industry sector, maritime security is concentrated on maritime transportation systems and connecting the origin and destination of goods as well as ensuring the passage of these goods without interference from all forms of crime. Maritime security related to maritime transportation is also defined as the efforts made by the owners, operators and administrators of merchant ships [ 10 ], port facilities, offshore installations and other marine organizations in protecting their ships from plunder, sabotage, piracy [ 11 ], theft and external attacks [ 2 ].…”