Publication

Abstract : Deep feature search has transpired as a sub-problem of large-scale Reverse Image Retrieval (RIR) algorithms in computer vision. In light of recent progress, transforming deep features into binary codes and employing various search strategies to delineate similar images has gained popularity. However, these binary codes cannot accurately define the similarity between two images, which is essential for image retrieval. Moreover, when retrieving closest images, the existing RIR approaches show a drop in precision value as increases. In this paper, we put forward a method, Deep Learning based Improved Reverse Image Retrieval (DLIRIR), which solves the RIR problem by utilizing a deep neural network, clustering methodology, and dimensionality reduction technique. Motivated by the need to prevent information loss in converting deep features to binary codes and reduce search time, we cluster image features and select some features from each cluster called Representative Points (RPs). These RPs are then compared to features of an image (query) to retrieve images similar to it from the database. The Euclidean distances of the query feature and the RPs indicate similarity between the query and the cluster of images represented by that RP. We reduce the number of comparisons by selecting as few RPs as possible. We evaluate the performance of DLIRIR by comparing it with other state-of-the-art algorithms in the RIR domain. Our method achieves a precision greater than 99% in benchmark datasets Caltech 256, Corel 10k, and Adaptiope, while a precision between 80%–85% on large size datasets Cifar 100, Tiny ImageNet, and VGGFace2.