Identical Image Retrieval using Deep Learning

TL;DR

This paper presents an approach for image retrieval using a fine-tuned BigTransfer model combined with K-Nearest Neighbors to efficiently find visually similar images, demonstrating high accuracy and low inference time.

Contribution

The paper introduces a novel image retrieval method leveraging BigTransfer features and KNN, achieving improved similarity search performance.

Findings

High accuracy in image similarity retrieval

Low inference time for real-time applications

Effective use of BigTransfer features with KNN

Abstract

In recent years, we know that the interaction with images has increased. Image similarity involves fetching similar-looking images abiding by a given reference image. The target is to find out whether the image searched as a query can result in similar pictures. We are using the BigTransfer Model, which is a state-of-art model itself. BigTransfer(BiT) is essentially a ResNet but pre-trained on a larger dataset like ImageNet and ImageNet-21k with additional modifications. Using the fine-tuned pre-trained Convolution Neural Network Model, we extract the key features and train on the K-Nearest Neighbor model to obtain the nearest neighbor. The application of our model is to find similar images, which are hard to achieve through text queries within a low inference time. We analyse the benchmark of our model based on this application.