An adaptive image processing system based on incremental learning for industrial applications

Abstract

Machine learning has been applied in image processing system for object recognition, inspection and measurement. It assumes that the provided training objects are representative enough to the real objects. However in real application, new (unlearned) objects always emerge over time, which may deviate from the trained (learned) objects. The conventional image processing system using machine learning is not able to learn and then recognize these new objects. In this paper, an incremental learning based image processing system is presented. The overall system consists of three layers: execution, learning and user. The conventional image processing system is constructed in execution layer. In learning layer, adviser and incremental learning are applied to generate a new classifier. The incremental learning is differentiated into different methodologies: data accumulation and ensemble learning. Through the adviser, a proper methodology can be recommended. User is able to interact with the system via user layer. Comparing to the conventional image processing system, the proposed system is robust in industrial applications, since it deals with the classification problems dynamically.