Parallel desires: unifying local and semantic feature representations in marine species images for classification

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.