Semantic-Aware Image Deduplication: Leveraging Object Recognition for Enhanced Accuracy

Image deduplication is a critical task in managing large-scale image databases, with applications ranging from content moderation to efficient storage solutions. Traditional methods often rely on pixel-level comparisons or perceptual hashing techniques, which may fail to capture semantic similarities between images. This paper proposes a novel approach to image deduplication that incorporates semantic information derived from object recognition algorithms. By leveraging state-of-the-art deep learning models for object detection and classification, our method achieves higher accuracy in identifying duplicate and near-duplicate images, even in cases where visual features may differ significantly. We present a comprehensive evaluation of our semantic-aware deduplication system on diverse datasets, demonstrating its superior performance compared to existing methods. Our findings suggest that integrating semantic understanding into image deduplication processes can significantly enhance accuracy and robustness, paving the way for more efficient and intelligent image management systems.