The combination of differential evolution and color attention for object recognition

Abstract

Color attention algorithm can improve the accuracy of object recognition significantly. In the algorithm, there are 3 parameters related to bin number of color features and shape features and the weight between color and shape, need to be set. Whether the parameters are set correctly or not poses a great impact on the accuracy of object recognition. However, searching suitable values in the parameters space related to color attention algorithm is a NP-hard problem and the parameters of the algorithm are set manually in recent research through which the right values are not guaranteed to be found. Recently, bio-inspired computing gains much attention for its advantages in complex optimization problems and differential evolution outperforms most other bio-inspired computing algorithms in divergence and stability in optimization problems. When taking the accuracy of object recognition as a fitness function, increasing the accuracy of object recognition is then an optimization problem to find the largest accuracy in parameter spaces. Therefore, we design the structure of the agent of differential evolution and take the classification accuracy with support vector machine algorithm as a fitness function. Then we use differential evolution to search the parameters space and find some suitable parameters for color attention algorithm successfully. Our experimental evaluation demonstrates that the accuracy of object recognition increases greatly with the right parameters.