Classifier learning from difficult data on the example of missing features

Abstract

This paper describes a novel method for classification based on a partly incomplete and low quality image data. The method has been tested on images with low quality areas such as partially corrupted, dark, or blurry images. Image data are converted to classifier input. As a result, we are dealing with missing data describing these areas, but the classifier was designed to work in the presence of missing data. The analysis of the literature shows that missing data have a negative impact on the operation of the learning algorithms. This field is still insufficient explored. In our method, we integrate complex image processing techniques, machine learning, and statistical methods under various contexts. The novelty of the presented approach is the structure of the classifier, which works in the presence of missing data on different principles than those used so far. Evaluation was performed on the basis of realistic images, collected before experiments. Multi-variant experimental protocols, specially designed in this work, confirmed the accuracy of the method. The obtained results help to better understand the methods of recognizing objects with incomplete data structures. Analysis techniques proposed in this paper are rapidly gaining in importance, so the proposed approach can be useful in many areas where the machine learning is employed.