  Image classification is one of the main research problems in computer vision
and machine learning. Since in most real-world image classification
applications there is no control over how the images are captured, it is
necessary to consider the possibility that these images might be affected by
noise (e.g. sensor noise in a low-quality surveillance camera). In this paper
we analyse the impact of three different types of noise on descriptors
extracted by two widely used feature extraction methods (LBP and HOG) and how
denoising the images can help to mitigate this problem. We carry out
experiments on two different datasets and consider several types of noise,
noise levels, and denoising methods. Our results show that noise can hinder
classification performance considerably and make classes harder to separate.
Although denoising methods were not able to reach the same performance of the
noise-free scenario, they improved classification results for noisy data.
