An Automatic Recognition System for Digital Collections of Indonesian Traditional Houses Using Convolutional Neural Networks for Cultural Heritage Preservation

Teny Handhayani, Ageng Hadi Pawening, J. Hendryli
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Abstract

Indonesia is one of the archipelago countries located in Asia and it has diverse cultures. In modern society, Indonesian traditional houses have become rare and need to be preserved. This research is conducted to build a digital collection and to develop an image-based automatic recognition system for Indonesian traditional houses. In this paper, the traditional house images are collected in several ways: on-site image captures, receiving images from volunteers, and collecting public images from Google. The dataset is limited to the collection of building shape images, excluding the interior design. The authors implement Convolutional Neural Networks (ConvNets) to build a model for an automatic recognition system. The experiments run some deep network models: VGG, DenseNet, Inception, Xception, MobileNetV2, NasNetMobile, and EfficientNet. The experiments involve 1526 images of 16 classes. EfficientNet-Lite0 outperforms other models and produces the average F1-score and accuracy of 90.1% and 91.8%, respectively. ConvNets also outperform conventional classifiers.
基于卷积神经网络的印尼传统房屋数字馆藏自动识别系统
印度尼西亚是位于亚洲的群岛国家之一,有着多元的文化。在现代社会中,印尼传统房屋已经变得罕见,需要保护。本研究旨在建立印尼传统房屋的数位馆藏,并开发一套基于影像的自动识别系统。在本文中,传统房屋图像的采集方式有:现场图像采集、志愿者接收图像、谷歌公共图像采集。该数据集仅限于建筑物形状图像的收集,不包括室内设计。作者利用卷积神经网络(ConvNets)建立了一个自动识别系统的模型。实验运行了一些深度网络模型:VGG、DenseNet、Inception、Xception、MobileNetV2、NasNetMobile和EfficientNet。实验涉及16个班级1526张图片。EfficientNet-Lite0优于其他模型,平均f1得分和准确率分别为90.1%和91.8%。卷积神经网络也优于传统的分类器。
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