Gesture Classification in Artworks Using Contextual Image Features

TL;DR

This paper introduces a method for recognizing gestures in artworks by combining local features with global image context, enhancing classification accuracy and contributing to art understanding and cultural heritage analysis.

Contribution

It presents a novel approach that integrates local and global features for gesture recognition in artworks, improving performance over existing methods.

Findings

Combining local and global features improves classification accuracy.

The method performs well across different backbone architectures.

Enhanced understanding of gestures in historical artworks.

Abstract

Recognizing gestures in artworks can add a valuable dimension to art understanding and help to acknowledge the role of the sense of smell in cultural heritage. We propose a method to recognize smell gestures in historical artworks. We show that combining local features with global image context improves classification performance notably on different backbones.