USAGE.md

How to run the code?

Let me show you how to use the code.

It can divided to two parts.

Part1: make your image database

When you clone the repository, it will look like this:

├── src/            # Source files
├── result/         # Results
├── USAGE.md        # How to use the code
└── README.md       # Intro to the repo

you need to add your images into a directory called database/, so it will look like this:

├── src/            # Source files
├── result/         # Results
├── USAGE.md        # How to use the code
├── README.md       # Intro to the repo
└── database/       # Directory of all your images

all your image should put into database/

In this directory, each image class should have its own directory

see the picture for details:

In my database, there are 25 classes, each class has its own directory,

and the images belong to this class should put into this directory.

Part2: run the code

I implement several algorithm, you can run it with python3.

For RGB histogram

python3 src/color.py

For daisy image descriptor

python3 src/daisy.py

For gabor filter

python3 src/gabor.py

For edge histogram

python3 src/edge.py

For histogram of gradient (HOG)

python3 src/HOG.py

For VGG19

You need to install pytorch0.2 to run the code

python3 src/vggnet.py

For ResNet152

You need to install pytorch0.2 to run the code

python3 src/resnet.py

Above are basic usage of my codes.

There are some advanced issue such as features fusion and dimension reduction,

the intro of these parts will be written further in the future :D

Appendix: feature fusion

I implement the basic feature fusion method -- concatenation.

Codes for feature fusion is written in fusion.py

In fusion.py, there is a class called FeatureFusion.

You can create a FeatureFusion instance with an argument called features.

For example, in fusion.py line140

fusion = FeatureFusion(features=['color', 'daisy'])
APs = evaluate_class(db, f_instance=fusion, d_type=d_type, depth=depth)

• The first line means to concatenate color featrue and daisy feature.
• The second line means to evaluate with the concatenated feature.

If you want to know the performance of all possible feature combination, look at fusion.py line122 for example

evaluate_feats(db, N=2, d_type='d1')

• Parameter N means how many feature you want to concatenate.
• Parameter d_type means the distance metric you want to use.
• Function evaluate_feats will generate a result file that record performances for all feature concatenation.

Author

Po-Chih Huang / @pochih