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A content-based image retrieval approach utilizing optimized intermediate layer features
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DOI：10.1007/s10586-026-06086-w.png)
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Abstract
En 中文
Content-Based Image Retrieval (CBIR) systems aim to retrieve images from databases based on their visual features, circumventing the limitations of traditional metadata-based approaches. This study introduces a novel approach that integrates features from Modified MobileNet and EfficientNetB0 to generate a comprehensive feature vector. By concatenating features from the intermediate and last layers of these models, the proposed approach captures both high-level semantic information and low-level details. The features extracted from Modified EfficientNetB0 have been optimized by using selective intermediate layer feature extraction. The selection of these intermediate layers has been performed by extensive experimentation to enhance feature-based representation and retrieval performance. The efficacy of the proposed approach is evaluated using similarity measures such as Euclidean distance and unsupervised nearest neighbors techniques on benchmark datasets, including Corel-1K and Caltech-101. The results demonstrate that the proposed method achieves 100% precision for the top 5 retrieved images on the Corel-1K dataset. Additionally, we assess the performance of various K-Nearest Neighbors (KNN) algorithms to identify the most effective approach for relevant retrieval while maintaining computational efficiency. Experiments on the COIL dataset further validate the qualitative performance of the proposed approach, outperforming existing retrieval methodologies in terms of mean average precision, mean average recall, F1-score, and average retrieval time. This approach presents a promising step towards advancing CBIR systems by optimizing feature extraction and retrieval strategies. To promote research transparency and reproducibility, the source code for this work is publicly available on GitHub at https://github.com/sunita2207/Optimization_of_intermediate_layers .
Keywords:
CBIR
Transfer learning
KNN
Hybrid feature fusion
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