Simple Image Retrieval System

Overview

This project builds a simple Image Search Engine using the Keras framework with ResNet-50 as the backbone network (pre-train model). This system is a computer system used for browsing, searching and retrieving images from a large database of digital images.

Method practice

Convolutional Neural Network (ResNet50 backbone) + Cosine Similarity.

System flowcharts

Structure of directory

├── feature_extractor.py
├── offline.py
├── __pycache__
│   └── feature_extractor.cpython-37.pyc
├── README.md
├── server.py
├── static
│   ├── feature_database
│   │   ├── concat_all_feature.npz
│   │   ├── lion.npz
│   │   ├── pig.npz
│   │   ├── ...
│   ├── image_database
│   │   ├── lion
│   │   │   ├── 0209bbf635.jpg
│   │   │   ├── 05d42c9bd8.jpg
│   │   │   ├── ...
│   │   ├── pig
│   │   │   ├── 00ddea87ad.jpg
│   │   │   ├── 02b26f142c.jpg
│   │   │   ├── ...
│   │   ├── ...
│   └── image_uploaded
└── templates
    └── index.html

Hardware configuration

- Memory: 8,0 GiB
- Processor: Intel® Core™ i5-8365U CPU @ 1.60GHz × 8
- OS: Ubuntu 22.04.1 LTS (64-bit)

Dataset

Animal Image Dataset (90 different categories, 60 images per categories): 5400 Animal Images

Setup

Python 3.7 is used in this repository.
Clone the repository:

$ git clone https://github.com/danghieuan/image-retrieval-system.git

Feature Extractor (Employing ResNet-50 as the backbone network)

$ cd image-retrieval-system
$ python offline.py

Demo on Flask API

$ python server.py

Todo

Research Product Quantization algorithm for larger-scale indexing.

Reference

CVPR 2020 Tutorial on Image Retrieval in the Wild, author: Yusuke Matsui and Takuma Yamaguchi and Zheng Wang.
He, Kaiming, et al. "Deep residual learning for image recognition." Proceedings of the IEEE conference on computer vision and pattern recognition. 2016.
Deep Learning with Python - François Chollet.
Changing the contrast and brightness of an image! - OpenCV.