Multi-Objective Genetic Programming with Redundancy-Regulations for Automatic Construction of Image Feature Extractors

Abstract

SUMMARYWe propose a new multi-objective genetic programming (MOGP) for automatic construction of image feature extraction programs (FEPs). The proposed method was originated from a well known multiobjective evolutionary algorithm (MOEA), i.e., NSGA-II. The key differences are that redundancy-regulation mechanisms are applied in three main processes of the MOGP, i.e., population truncation, sampling, and offspring generation, to improve population diversity as well as convergence rate. Experimental results indicate that the proposed MOGP-based FEP construction system outperforms the two conventional MOEAs (i.e., NSGA-II and SPEA2) for a test problem. Moreover, we compared the programs constructed by the proposed MOGP with four human-designed object recognition programs. The results show that the constructed programs are better than two human-designed methods and are comparable with the other two human-designed methods for the test problem.

Feature Learning for Image Classification Via Multiobjective Genetic Programming

“…Their method has been applied to edge detection problems for image processing and achieved a comparable result with the Canny edge detector. Similarly, Watchareeruetai et al [38] have also adopted the MOGP for automatic construction of feature extractor. Liddle et al [39] have proposed a MOGP approach for the task of providing a decision-maker with a diverse set of alternative object detection programs that balance between high detection rate and low false-alarm rate.…”