{"title":"Parallel desires: unifying local and semantic feature representations in marine species images for classification","authors":"Dhana Lakshmi Manikandan, Sakthivel Murugan Santhanam","doi":"10.1007/s11001-024-09551-6","DOIUrl":null,"url":null,"abstract":"<p>Accurate identification of marine species is essential for ecological monitoring, habitat assessment, biodiversity conservation, and sustainable resource management. To address the challenges associated with diverse and complex marine environments, the paper proposes a integrated model that combines the strengths of a Vision Transformer (ViT) and Transfer Learning (TL). The paper introduces a novel methodology for the classification of marine species images by integrating the capabilities of a Amended Dual Attention oN Self-locale and External (ADANSE) Vision Transformer and a DenseNet-169 Transfer Learning model. The ADANSE-ViT, serving as the foundational architecture, excels in capturing long-range dependencies and intricate patterns in large-scale images, forming a robust basis for subsequent classification tasks. On Fine-tuning further, it customizes the model for marine species images. Additionally, we utilize transfer learning with the DenseNet-169 architecture, pre-trained on a comprehensive dataset, to extract relevant features and enhance classification effectiveness specifically for marine species. This synergistic combination enables a comprehensive analysis of both local and semantic features in species images, leading to accurate classification results. Experimental evaluations conducted on self-collected and benchmark datasets showcase the efficacy of our approach, surpassing existing fish classifiers and TL variants in terms of classification accuracy. Our integrated model achieves an impressive accuracy of 96.21% for the self-collected dataset and 95.09% for the benchmarked dataset.</p>","PeriodicalId":49882,"journal":{"name":"Marine Geophysical Research","volume":null,"pages":null},"PeriodicalIF":1.6000,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Marine Geophysical Research","FirstCategoryId":"89","ListUrlMain":"https://doi.org/10.1007/s11001-024-09551-6","RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"GEOCHEMISTRY & GEOPHYSICS","Score":null,"Total":0}
引用次数: 0
Abstract
Accurate identification of marine species is essential for ecological monitoring, habitat assessment, biodiversity conservation, and sustainable resource management. To address the challenges associated with diverse and complex marine environments, the paper proposes a integrated model that combines the strengths of a Vision Transformer (ViT) and Transfer Learning (TL). The paper introduces a novel methodology for the classification of marine species images by integrating the capabilities of a Amended Dual Attention oN Self-locale and External (ADANSE) Vision Transformer and a DenseNet-169 Transfer Learning model. The ADANSE-ViT, serving as the foundational architecture, excels in capturing long-range dependencies and intricate patterns in large-scale images, forming a robust basis for subsequent classification tasks. On Fine-tuning further, it customizes the model for marine species images. Additionally, we utilize transfer learning with the DenseNet-169 architecture, pre-trained on a comprehensive dataset, to extract relevant features and enhance classification effectiveness specifically for marine species. This synergistic combination enables a comprehensive analysis of both local and semantic features in species images, leading to accurate classification results. Experimental evaluations conducted on self-collected and benchmark datasets showcase the efficacy of our approach, surpassing existing fish classifiers and TL variants in terms of classification accuracy. Our integrated model achieves an impressive accuracy of 96.21% for the self-collected dataset and 95.09% for the benchmarked dataset.
期刊介绍:
Well-established international journal presenting marine geophysical experiments on the geology of continental margins, deep ocean basins and the global mid-ocean ridge system. The journal publishes the state-of-the-art in marine geophysical research including innovative geophysical data analysis, new deep sea floor imaging techniques and tools for measuring rock and sediment properties.
Marine Geophysical Research reaches a large and growing community of readers worldwide. Rooted on early international interests in researching the global mid-ocean ridge system, its focus has expanded to include studies of continental margin tectonics, sediment deposition processes and resulting geohazards as well as their structure and stratigraphic record. The editors of MGR predict a rising rate of advances and development in this sphere in coming years, reflecting the diversity and complexity of marine geological processes.