ACO Image Feature Extraction

version 1.0.0 (145 KB) by Seyed Muhammad Hossein Mousavi
Ant Colony Optimization (ACO) Image Feature Extraction Method


Updated 1 Jan 2022

View License

%% 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:
% Author: Seyed Muhammad Hossein Mousavi
% My MathWorks:
% My GitHub:
% 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 :)

Cite As

Seyed Muhammad Hossein Mousavi (2022). ACO Image Feature Extraction (, MATLAB Central File Exchange. Retrieved .

MATLAB Release Compatibility
Created with R2019a
Compatible with any release
Platform Compatibility
Windows macOS Linux

Community Treasure Hunt

Find the treasures in MATLAB Central and discover how the community can help you!

Start Hunting!