Feature Optimization Techniques in Image Classification : A Review
This paper has been written with the objective of studying and understanding the various feature optimization techniques in the field of image processing. Feature optimization is essential for reducing the number of input variables to in turn reduce the computational cost and to improve the performance of the model. In image processing, there exist a vast number of different optimization techniques ranging from heavily mathematical techniques to heuristic methods. This project deals with the discussion of six feature optimization techniques and their comparison. These techniques include Genetic Algorithm, Ant Colony Optimization, Particle Swarm Optimization, Cuckoo Search and Artificial Bee Colony Algorithm. Mainly, the paper aims to discuss the aforementioned algorithms stating their advantages, disadvantages and applications.
Keywords - Algorithm, Feature Extraction, Optimization, Population, Solution