Museum Painting Retrieval

Abstract

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.

Stats

• citations0
• S2 citations—
• github stars0
• HF likes0
• heat score0.00
• arxiv keylorente2021museum

Related papers

• Visual Link Retrieval And Knowledge Discovery In Painting Datasets (2020)12.25
• Object Retrieval And Localization In Large Art Collections Using Deep Multi-style Feature Fusion And Iterative Voting (2021)7.50
• How To Read Paintings: Semantic Art Understanding With Multi-modal Retrieval (2018)13.93
• Leveraging Computer Vision Application In Visual Arts: A Case Study On The Use Of Residual Neural Network To Classify And Analyze Baroque Paintings (2022)3.58
• Challenging Deep Image Descriptors For Retrieval In Heterogeneous Iconographic Collections (2019)5.84
• Image Retrieval Approach Based On Local Texture Information Derived From Predefined Patterns And Spatial Domain Information (2019)0.00
• Linking Art Through Human Poses (2019)10.74
• Dare To Plagiarize? Plagiarized Painting Recognition And Retrieval (2025)0.00