Contact : [email protected]
An AI based Image Retrieval System In this project we will build an information retrieval system. We have a query image and we want to retrieve the most visually similar image for a given dataset. The main idea is to extract relevant vectorial features from images in order to measure a meaningfull similarity distance.
The demo.iypnb file is a notebook that contains all the workflow for the project, with details, comments and results, therefore you can use it as an example and reuse some pieces of codes in your pipelines scripts.
If you are interrest in executing this demo.iypnb notebook and reproduce the results.
- 1- Fork the repository
- 2- Download the dataset (link is in the notebook)
- 3- Replace the path of the import cell (commented on the notebook)
https://en.wikipedia.org/wiki/Content-based_image_retrieval https://towardsdatascience.com/bag-of-visual-words-in-a-nutshell-9ceea97ce0fb https://towardsdatascience.com/review-resnet-winner-of-ilsvrc-2015-image-classification-localization-detection-e39402bfa5d8
One shot learning consists in learning from one (or a few) examples. It is a concept closely related to image retrieval. If you are interested in siamese networks for image retrieval, you can read these 3 blog posts, which mostly focus about one shot learning: https://hackernoon.com/one-shot-learning-with-siamese-networks-in-pytorch-8ddaab10340e https://sorenbouma.github.io/blog/oneshot/ https://towardsdatascience.com/one-shot-learning-face-recognition-using-siamese-neural-network-a13dcf739e