ACO Image Feature Extraction

Ant Colony Optimization (ACO) Image Feature Extraction Method

%% Ant Colony Optimization (ACO) Image Feature Extraction Method

% Feature Extraction and Feature Selection are two different tasks. Feature

% Extraction is initial and vital step, but feature selection is optional.

% There are lots of evolutionary feature selection code are online for

% MATLAB but not feature extraction, especially for image. This code extracts

% features out of 10 classes of images with Ant Colony Optimization (ACO)

% evolutionary algorithm and compared it with extracted features using

% SURF with KNN classifier. Dataset is consists of 100 samples of small

% objects in 10 classes. You can use your data but labeling is done manually

% which you have to change it. following parameters are so important which

% you have to play with them in order to get desired results. Parameters

% are: 'nf', 'MaxIt', 'nAnt', knn classifier neighbors and number of hidden

% layers in "TrainNN.m" file. Feel free to contact me:

% Email: [e-mail]

% Author: Seyed Muhammad Hossein Mousavi

% My MathWorks: https://www.mathworks.com/matlabcentral/profile/authors/9763916

% My GitHub: https://github.com/SeyedMuhammadHosseinMousavi

% This code is part of the following project, so if you used the code,

% please cite below paper:

% Mousavi, Seyed Muhammad Hossein, S. Younes MiriNezhad, and Mir Hossein Dezfoulian.

% "Galaxy gravity optimization (GGO) an algorithm for optimization, inspired by comets

% life cycle." 2017 Artificial Intelligence and Signal Processing Conference (AISP).

% IEEE, 2017.

% Hope it help you (Be Happy :)