DFFnet: delay feature fusion network for efficient content-based image retrieval

Abstract

Due to advancement of affordable imaging devices, a huge number of images are generated for different applications. An efficient method for retrieving the appropriate images corresponding to the query image from a huge repository is still awaited. Thus, content-based image retrieval (CBIR) systems have been developed. One of the issues that directly threatens the effectiveness of CBIR system is a semantic gap. In this paper, we introduce a Delay Feature Fusion Network (DFFnet) in the framework of SqueezeNet architecture. Our proposed model fuses the past layer’s features with the current layer’s features by utilizing a transpose convolution operation followed by depth-concatenation. This integration preserves the crucial information that may be lost during the forward pass. After extracting image features, we apply the t-SNE (t-Distributed Stochastic Neighbor Embedding) method. This technique allows us to project the high-dimensional image features into a lower-dimensional space, enabling compact image indexing and potentially improving the overall performance of CBIR system. Notably, we observed that as the number of retrieval rates increases, our proposed method experiences minimal impact. By leveraging the DFFnet and employing t-SNE, our approach aims to enhance image indexing and achieve improved performance for image retrieval tasks. The performance of DFFnet with t-SNE and without t-SNE are evaluated on benchmark datasets—Corel, Kadid, and ImageNet. Our proposed DFFnet with t-SNE gives a significant improvement in terms of performance metrics: precision, recall, and F1-score, in comparison to other state-of-the-arts. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.