Degradation adaptive texture classification for real-world application scenarios

Images captured under non-laboratory conditions potentially suffer from various degradations. Especially noise, blur and scale-variations are often prevalent in real world images and are known to potentially affect the classification process of textured images. We show that these degradations not necessarily strongly affect the discriminative powers of computer based classifiers in a scenario with similar degradations in training and evaluation set. We propose a degradation-adaptive classification approach, which exploits this knowledge by dividing one large data set into several smaller ones, each containing images with some kind of degradation-similarity. In a large experimental study, it can be shown that our method continuously enhances the classification accuracies in case of simulated as well as real world image degradations. Surprisingly, by means of a pre-classification, the framework turns out to be beneficial even in case of idealistic images which are free from strong degradations.