A Deep Learning Approach for Content-Based Image Retrieval

Content-Based Image Retrieval (CBIR) aims to retrieve relevant images based on visual content rather than metadata, addressing the limitations of traditional retrieval methods. This study proposes a deep learning-based CBIR system utilizing Convolutional Neural Networks (CNNs) for automatic feature extraction. Leveraging the CIFAR-10 dataset, the system is evaluated against traditional handcrafted methods such as color histograms and color moments. Various retrieval paradigms image-based, text-based, sketch-based, and conceptual layout are analyzed for performance comparison. Experimental results demonstrate that CNN-based retrieval achieves over 85% accuracy, significantly outperforming traditional approaches. The system exhibits robustness to intra-class variation, occlusion, and background noise, establishing deep learning as a superior and scalable approach for large-scale CBIR applications. Keywords—Content-Based Image Retrieval, Deep Learning, CNN, Image Similarity, CIFAR-10, Feature Extraction, Image Retrieval Algorithms