Pattern Analysis in Marine Data Classification and Recognition

Abstract

With the advent of upcoming new patterns to handle (e.g., big data and semantic annotation), to encourage research to detect and identify objects, the development of the internet of things in the ocean requires the interconnection of all equipment (sensors) to observe the oceans. By serving as a common conceptualization among these tools, marine ontologies can lead to lower costs and better flexibility in marine data recognition and classification. To that end, marine pattern analysis literature (1991-2021) is used to create a sample network of records, comprising visual and textual features that can be annotated from video and image sequences, with the underwater parameters as the target of interest. The sample is split into ontological and machine learning (ML) datasets to build a prediction of the importance of data visualization techniques. The predicted suitability is strong with data classification that belongs to the machine learning dataset. However, the initial results from the study are encouraging, because ontologies' tools are proposed as automatic reasoning mechanisms.