This is a tough problem and therefore there are many approaches.
On common method (although complicated) is taken an input image, superpixelate the image and compute descriptors (such as SIFT of SURF) of those superpixels building a bag-of-word representation by accumulating histograms per superpixel, this operation extracts the key information from a bunch of pixels reducing dimensionality. Then a Conditional Random Field algorithm searches for relationships between superpixels in the image and classifies the group of pixels inside a known category. For pixelating images scikit-image package implements SLIC algorithm segmentation.slic, and for the CRF you should take a look to PyStruct package. SURF and SIFT can be calculated using OpenCV.
Another simple version would be computing descriptors of a given image (SIFT, SURF, borders, histogram etc) and use them as inputs in a classifier algorithm, you might want start from here, maybe scikit-learn.org is the easiest and most powerful package for doing this.
