Intelligent object recognition in underwater images using evolutionary-based Gaussian mixture model and shape matching

Object recognition in underwater images becomes a challenging task because of its poor visibility conditions. Marine scientists often prefer automation tools for object recognition as large amount of data is captured everyday with the help of autonomous underwater vehicles. The challenge for classification in such underwater images is the limited color information. An attempt is made to recognize objects in underwater images using an adaptive Gaussian mixture model. The Gaussian mixture model performs accurate object segmentation provided the number of clusters is predefined. Optimization techniques like genetic algorithm, particle swarm optimization and differential evolution were analyzed for initializing the parameter set. Differential evolution is known for its accurate decision making in fewer iterations and proved to be better for initializing the number of clusters for the Gaussian mixture model. Further for object recognition, inner distance shape matching technique was applied. The proposed classification method achieved a maximum accuracy of 99%.