A segment-wise prediction based on genetic algorithm for object recognition

Object recognition in complex backgrounds has challenged the fields of pattern recognition for years. It is even harder when the targets in images are of different poses. Current methods use descriptors of characteristic vectors and machine learning algorithms to produce classifiers for object recognition. However, the generalization ability of these methods relies on the quality of the training phase and cannot find the precise boundaries of the targets. The geometric features of objects are the most stable and consistent features, so the recognition method based on shapes can be more intuitive than those based on color and texture features. This paper proposes a novel method that uses the images represented by line segments. The recognition mission becomes to effectively filter and combine them. The contour fragments after combinations are expected to satisfy the given model, or certain parts of it. In this way, object recognition can be viewed as a combinatorial optimization problem. This paper develops a genetic algorithm-based method to solve this problem. The experimental results show that this method can solve the combinatorial optimization problem effectively and can accurately distinguish the contour of the target object from the background. This method, which is based on geometric features, may contribute to the development of explicit principals for the description of object structure and recognition method based on symbolic reasoning.