https://github.com/pochih/CBIR

🏞 A content-based image retrieval (CBIR) system
https://github.com/pochih/CBIR

computer-vision daisy edges feature-fusion gabor histogram hog image-retrieval random-projection resnet vggnet

Last synced: 3 months ago
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🏞 A content-based image retrieval (CBIR) system

• Host: GitHub
• URL: https://github.com/pochih/CBIR
• Owner: pochih
• Created: 2017-11-07T11:07:59.000Z (about 7 years ago)
• Default Branch: master
• Last Pushed: 2022-07-07T18:07:55.000Z (over 2 years ago)
• Last Synced: 2024-08-01T03:46:10.439Z (6 months ago)
• Topics: computer-vision, daisy, edges, feature-fusion, gabor, histogram, hog, image-retrieval, random-projection, resnet, vggnet
• Language: Python
• Homepage:
• Size: 2.88 MB
• Stars: 609
• Watchers: 16
• Forks: 211
• Open Issues: 12
• Metadata Files:
  • Readme: README.md

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README

        
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## Intro 

__This repository contains a CBIR (content-based image retrieval) system__

__Extract query image's feature, and retrieve similar ones from image database__



Image src


## 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/)