the result is that each image is now represented by single array of features of the same size (the number of clustersin our case thereforewe can use our standard classification methods using logistic regression againwe now get percenta percent improvement we can combine all of the features together and we obtain percentmore than percent over what was obtained with texturebased methodssummary we learned the classical feature-based approach to handling images in machine learning context by reducing million pixels to few numeric dimensions all the technologies that we learned in the other suddenly become directly applicable to image problems this includes classificationwhich is often referred to as pattern recognition when the inputs are imagesclusteringor dimensionality reduction (even topic modeling can be performed on imagesoften with very interesting resultswe also learned how to use local features in bag-of-words model for classification this is very modern approach to computer vision and achieves good results while being robust to many irrelevant aspects of the imagesuch as illumination and also uneven illumination in the same image we also used clustering as useful intermediate step in classification rather than as an end in itself