EDICA: A Hybrid Ensemble Architecture Using Deep Learning Models for Fine-Grained Image Classification
Year: 2025
Type: article
Abstract: This work presents EDICA, a two-stage architecture for fine-grained image classification, which is a hybrid model for the detection and classification task. The model employs YOLOv8 for the detection stage and an ensemble deep learning model that utilizes a majority voting strategy for fine-grained image classification. The proposed model aims to enhance the precision of classification by integrating classification models that have been trained with the same classes. This approach enables the utilization of the strengths of these classification models for a range of test instances. The experiment involved a diverse set of classes, encompassing a variety of types, including dogs, cats, birds, fruits, frogs, and foliage; each class is divided into subclasses for finer-grained classification, such as specific dogs, cat breeds, bird species, and fruit types. The experimental results show that the hybrid model outperforms classification approaches that use only one model, thereby demonstrating greater robustness relating to ambiguous complex images and uncontrolled environments.
Source: Mathematics
DOI: 10.3390/math13223729
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,137
OpenAlex ID: W4416420693