An Efficient Feature Extraction and Image Retrieval Based on Convolutional Neural Networks
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Abstract
Image retrieval has become increasingly important in many application domains, including medicine, forensics, biology, astronomy, fashion, and military systems, where large volumes of digital images are generated and stored daily. Consequently, there is a growing need to retrieve images based on their visual content, such as color, texture, and shape, rather than relying on keywords or textual annotations that may be incomplete or unavailable. Content-Based Image Retrieval (CBIR) has been developed to address this challenge. However, designing effective image features remains difficult due to the semantic gap between human perception and machine-level representation. This research proposes a novel hybrid feature extraction and fusion approach for image retrieval, termed Fuse Convolutional Neural Network Models (FCNNM). The proposed method leverages multiple layers of Convolutional Neural Networks (CNNs) to learn discriminative image representations while avoiding the high dimensionality and computational complexity associated with dense layers in traditional Deep CNNs. Instead, the model relies on sequential convolutional and pooling layers. Two CNN architectures with three and four layers, respectively, are designed, and their extracted features are fused to improve retrieval performance. The proposed approach is evaluated using benchmark datasets, namely Corel-1K and Corel-5K. Experimental results demonstrate that fusing 2D CNN 3- and 4-layer models significantly enhances retrieval accuracy, achieving 97% on Corel-1K and 95% on Corel-5K, while maintaining high computational efficiency.
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