CLASSIFICATION OF CULTURAL HERITAGES IN THE MUSEUMCOLLECTIONS ON CENTRAL SULAWESI, INDONESIA USING DEEPLEARNING
Gazali Lembah††
Tadulako University, Indonesia‡Ade Nurul Izatti‡
Tadulako University, indonesia

Iksam Djorimi††††Museum of Central Sulawesi, Indonesia‡‡‡ABSTRACTThis paper discusses the cultural heritages stored in the Central Sulawesi Museum in Palu, Indonesia. Typically, thesecollections are displayed in the collection room with relatively limited information. Museum visitors generally wantto get more information regarding the origin, materials, orother aspects considering that collection. If the cultural heritage is systematically arranged, it is ensured that it will
provide more exciting information. This research utilizes
a deep learning framework to explore new ways to represent the museum collections. We focus on the classificationof cultural heritages in the public Museum in Central Sulawesi, Indonesia. There are about 7.595 item collectionsclassify into ten categories. In this research, we observed 8of 10 categories only due to the very limited available data
to be acquired. We confirmed that our approach shows adequate performance along with all the categories in sense ofvery little data to be trained.
Keywords: museum, collection, cultural heritage, classification, deep learning

1. INTRODUCTION
Museum of Central Sulawesi is a non-profit government organization destined for serving and open to the public. The
main task of the museum is to store, care for, secure, and utilize the museum collection in the form of cultural heritage.
While, the museum’s function is to preserve (save, managefor and protect) and as a source of information (research,education, and entertainment). Since the various collectionsof the museum, including the cultural heritage, are valuablehistorical objects, it is necessary to make efforts to makethese collections attractive to visitors but still maintainedeven though the public can witness them at any moment.
One of the drawbacks of organizing these collections is thelack of information that visitors can acquire about the cultural heritage in the display gallery.

Andi Hendra‡‡‡Toyohashi University of Technology, Japan
Nowadays, advance technologies in computer visionpresent a huge opportunity for many researchers, including cultural practitioners, to develop new ways of working
with museum collections. Deep Learning as a powerful toolseeks to explore how machine learning can be employed todevelop a deep neural networks model that can be helpful
to classify the cultural heritages collection in the museum.
Belhi et al. [1] introduced a very encouraging work basedon deep learning using residual network to classify and annotated the cultural heritage data. In addition, they also utilized the advantage of adversarial network for completing
missing data of the valuable cultural heritage collections.
2. OVERVIEW
In this section, we describe about the cultural heritages
available in the Central Sulawesi Museum and our deep neural networks architecture for the classification method.
2.1. CULTURAL HERITAGESIn the Museum of Central Sulawesi, Indonesia, there areabout 7.595 item of cultural collections [4]. All those cultural heritages are classified into ten main categories; geology, biology, ethnography, archeology, history, numismatic,philology, technology, ceramology, and art. In this research,
we examined only eight out of ten collection categories because of the deficiency of collecting data to be processed in
the deep neural network.
2.2. DEEP NEURAL NETWORKS
We present a deep neural network model as a classification
tool for our research data in cultural heritages encouragedby the work of [1]. Moreover, an innovative training criterion of depth neural network for maximum interval minimum classification error has been proposed in [3] to boost
the performance of the deep learning method. In our work,
we propose a simple network architecture rather than a complex one but maintains to get reasonable accuracy perfor-archeology

biologyethnography

geologyhistory

numismatics

philology

technology

Figure 1: Sample original images and their class taken from Central Sulawesi Museum, Indonesia.
mance. since we have very limited data to be trained, weemploy a small VGG-16 network, contains thirteen convolutional layers, which has been pre-trained on ImageNet.
In addition, we take advantage of transfer learning without
having to start the model from scratch by training a large
model on a large dataset.

3. IMPLEMENTATION
To demonstrate our approach’s performance, we present
several experiments exploring different aspects of our approach. We train our model using a dataset of cultural museum collections. Since the number of data for each class isnot quite the same, we trained our model using 350 images
and validated on 50 images for each class.

3.1. DATASETS
Experiments were performed on dataset museum collectionof Central Sulawesi, which was acquired using a camera
with resolution 4032×3024. Since several objects are included in one shoot picture, we crop the original image intosome small images and resize them into 175×125 while
training the input image. Some sample data from each class
we use in this research are shown in Fig. 1.

3.2. TRAINING DETAILSWe train our model on deep learning Keras [2] frameworkusing Tensorflow backend. Since we use a smaller networkarchitecture and the data to be trained is relatively small, wetrain our model on macOS Catalina 10.15 without need an
expensive GPUs. We also apply data augmentation via a
number of random transformations to help the model generalize better.

3.3. RESULT AND DISCUSSION
We trained our network for 55 epochs, and the approachgets us to a validation accuracy of around 0.82 which ispretty good for such a small dataset. We observed thatfrom all classes, the archeology class reach the highest accuracy (0.84), and the second place is geology (0.81). Whilethe lowest accuracy is the class of ethnography collection(0.72).
We analyzed the result and observed several limitations toour model approach. Our concern is to be overfitting since
we only have very few sample data to be trained. Anotherconcern to be affected the accuracy performance is the similarity of the object, either color or/and their shape, from different category collections. We observed that our model islikely predicting the significant amount of brown color (soilcolor) in our input image as archeology class in these typesof situations. So, the ethnographic collection (i.e., machete)mostly wrong classified due to the similar shape and colorof the history collection (i.e., sword). We also provide an
example of correct and incorrect predicted class In Fig. 2
to demonstrate our proposed approach and show the limitation to our image classification in the museum collections
in Central Sulawesi, Indonesia.
4. CONCLUSION AND FUTURE WORKSIn this paper, we proposed a new approach in working withthe cultural data in Central Sulawesi Museum of Indonesia
in the term of classification using a deep learning architecture. Our model achieves a moderately good performance
considering the very little data use to train our network. Inaddition, we observed the limitation of our approach due tothe characteristic of the object’s color or/and shape.
Our result accomplished a promising performance, whichis encouraging our future work will improve the performance and develop a simple and robust application for clas-

Figure 2: Random sample predicted class of the cultural data in Central Sulawesi Museum, Indonesia. The green text is thepredicted label, while the red text is the ground truth label.
sifying and annotating the precious cultural heritage in theCentral Sulawesi Museum, Indonesia.
5. REFERENCES[1] A. Belhi, H. Gasmi, A.K. Al-Al, A. Bouras, S. Foufou,
X. Yu, H. Zhang: “Deep Learning and Cultural Heritage: The CEPROQHA Project Case Study’ Proc.
of International Conference on Software, Knowledge,
Information Management and Applications (SKIMA),
pp.1-5 (2019).
[2] Chollet, Fran, Others: “Keras” Ihttps://keras.io, accessed April 30 (2021).
[3] Xin, M., Wang, Y.: “Research on Image Classification
Model Based on Deep Convolution Neural Network”
Proc. of J Image Video., Vol.40, pp. 1687-5281 (2019).
[4] S. Sri Ramlah: “Buku Pedoman Berkunjung Museum
Sulawesi Tengah” Dinas Pendidikan dan Kebudayaan
Provinsi Sulawesi Tengah, (2020).