Identical Image Retrieval using Deep Learning
This work addresses image retrieval for users needing fast visual similarity searches, but it is incremental as it applies existing methods like BigTransfer and K-Nearest Neighbors to this task.
The paper tackles the problem of retrieving visually similar images using a query image, achieving low inference time for applications where text queries are insufficient.
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.