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

An empirical study on the effects of different types of noise in image classification tasks