EffNet-CNN: A Semantic Model for Image Mining & Content-Based Image Retrieval

Content-Based Image Retrieval (CBIR) and image mining are becoming more important study fields in computer vision due to their wide range of applications in healthcare, security, and various domains. The image retrieval system mainly relies on the efficiency and accuracy of the classification models. This research addresses the challenge of enhancing the image retrieval system by developing a novel approach, EfficientNet-Convolutional Neural Network (EffNet-CNN). The key objective of this research is to evaluate the proposed EffNet-CNN model’s performance in image classification, image mining, and CBIR. The novelty of the proposed EffNet-CNN model includes the integration of different techniques and modifications. The model includes the Mahalanobis distance metric for feature matching, which enhances the similarity measurements. The model extends EfficientNet architecture by incorporating additional convolutional layers, batch normalization, dropout, and pooling layers for improved hierarchical feature extraction. A systematic hyperparameter optimization using SGD, performance evaluation with three datasets, and data normalization for improving feature representations. The EffNet-CNN is assessed utilizing precision, accuracy, F-measure, and recall metrics across MS-COCO, CIFAR-10 and 100 datasets. The model achieved accuracy values ranging from 90.60% to 95.90% for the MS-COCO dataset, 96.8% to 98.3% for the CIFAR-10 dataset and 92.9% to 98.6% for the CIFAR-100 dataset. A validation of the EffNet-CNN model’s results with other models reveals the proposed model’s superior performance. The results highlight the potential of the EffNet-CNN model proposed for image classification and its usefulness in image mining and CBIR.