Historical and Modern Features for Buddha Statue Classification

TL;DR

This paper introduces an automatic method to analyze Buddha statues by extracting construction-based proportions and comparing deep learning features for classification, aiding art history experts in identifying and studying displaced or varied artworks.

Contribution

It proposes a novel approach combining canon-based proportions with deep learning features for classifying Buddha statues, supported by a curated expert-annotated dataset.

Findings

Deep learning features outperform traditional methods in classification accuracy.

Proportions based on construction guidelines improve interpretability.

The dataset enables robust evaluation of automatic Buddha statue analysis.

Abstract

While Buddhism has spread along the Silk Roads, many pieces of art have been displaced. Only a few experts may identify these works, subjectively to their experience. The construction of Buddha statues was taught through the definition of canon rules, but the applications of those rules greatly varies across time and space. Automatic art analysis aims at supporting these challenges. We propose to automatically recover the proportions induced by the construction guidelines, in order to use them and compare between different deep learning features for several classification tasks, in a medium size but rich dataset of Buddha statues, collected with experts of Buddhism art history.