  To retrieve images based on their content is one of the most studied topics
in the field of computer vision. Nowadays, this problem can be addressed using
modern techniques such as feature extraction using machine learning, but over
the years different classical methods have been developed. In this paper, we
implement a query by example retrieval system for finding paintings in a museum
image collection using classic computer vision techniques. Specifically, we
study the performance of the color, texture, text and feature descriptors in
datasets with different perturbations in the images: noise, overlapping text
boxes, color corruption and rotation. We evaluate each of the cases using the
Mean Average Precision (MAP) metric, and we obtain results that vary between
0.5 and 1.0 depending on the problem conditions.
