# CBIR
**Repository Path**: xkeys1997/CBIR
## Basic Information
- **Project Name**: CBIR
- **Description**: A content-based image retrieval (CBIR) system
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No
## Statistics
- **Stars**: 0
- **Forks**: 0
- **Created**: 2020-05-08
- **Last Updated**: 2020-12-19
## Categories & Tags
**Categories**: Uncategorized
**Tags**: None
## README
[](https://github.com/ellerbrock/open-source-badges/)
## Intro
__This repository contains a CBIR (content-based image retrieval) system__
__Extract query image's feature, and retrieve similar ones from image database__
## Part1: feature extraction
In this system, I implement several popular image features:
- color-based
- [RGB histogram](https://github.com/pochih/CBIR/blob/master/src/color.py)
- texture-based
- [gabor filter](https://github.com/pochih/CBIR/blob/master/src/gabor.py)
- shape-based
- [daisy](https://github.com/pochih/CBIR/blob/master/src/daisy.py)
- [edge histogram](https://github.com/pochih/CBIR/blob/master/src/edge.py)
- [HOG (histogram of gradient)](https://github.com/pochih/CBIR/blob/master/src/HOG.py)
- deep methods
- [VGG net](https://github.com/pochih/CBIR/blob/master/src/vggnet.py)
- [Residual net](https://github.com/pochih/CBIR/blob/master/src/resnet.py)
##### *all features are modulized*
### Feature Fusion
Some features are not robust enough, and turn to feature fusion
- [fusion.py](https://github.com/pochih/CBIR/blob/master/src/fusion.py)
### Dimension Reduction
The curse of dimensionality told that vectors in high dimension will sometimes lose distance property
- [Random Projection](https://github.com/pochih/CBIR/blob/master/src/random_projection.py)
## Part2: evaluation
CBIR system retrieves images based on __feature similarity__
Robustness of system is evaluated by MMAP (mean MAP), the evaluation formulas is refer to here
- image AP : average of precision at each hit
- depth=K means the system will return top-K images
- a correct image in top-K is called a hit
- AP = (hit1.precision + hit2.precision + ... + hitH.precision) / H
- class1 MAP = (class1.img1.AP + class1.img2.AP + ... + class1.imgM.AP) / M
- MMAP = (class1.MAP + class2.MAP + ... + classN.MAP) / N
Implementation of evaluation can found at [evaluate.py](https://github.com/pochih/CBIR/blob/master/src/evaluate.py)
My database contains 25 classes, each class with 20 images, 500 images in total, depth=K will return top-K images from database
Method | color | daisy | edge | gabor | HOG | vgg19 | resnet152
--- | --- | --- | --- |--- |--- |--- |---
Mean MAP (depth=10) | 0.614 | 0.468 | 0.301 | 0.346 | 0.450 | 0.914 | 0.944
## Part3: image retrieval (return top 5 of each method)
Let me show some results of the system
### query1 - women dress
#### query 
#### color 
#### daisy 
#### edge 
#### gabor 
#### HOG 
#### VGG19 
#### Resnet152 
### query2 - orange
#### query 
#### color 
#### daisy 
#### edge 
#### gabor 
#### HOG 
#### VGG19 
#### Resnet152 
### query3 - NBA jersey
#### query 
#### color 
#### daisy 
#### edge 
#### gabor 
#### HOG 
#### VGG19 
#### Resnet152 
### query4 - snack
#### query 
#### color 
#### daisy 
#### edge 
#### gabor 
#### HOG 
#### VGG19 
#### Resnet152 
## Part4: usage of repository
If you are interesting with the results, and want to try your own images,
Please refer to [USAGE.md](https://github.com/pochih/CBIR/blob/master/USAGE.md)
The details are written inside.
## Author
Po-Chih Huang / [@pochih](http://pochih.github.io/)