Museum Painting Retrieval
This is an incremental study for museum curators or researchers, applying existing methods to a specific domain with varied conditions.
The paper tackled the problem of retrieving museum paintings using classic computer vision techniques, achieving Mean Average Precision (MAP) scores ranging from 0.5 to 1.0 depending on image perturbations like noise and rotation.
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.