Optimization of Historic Buildings Recognition: CNN Model and Supported by Pre-processing Methods

Abdul Rangkuti - Bina Nusantara University, Jakarta,11480, Indonesia
Varyl Hasbi Athala - Bina Nusantara University, Jakarta,11480, Indonesia
Farrel Haridhi Indallah - Bina Nusantara University, Jakarta,11480, Indonesia
Evawaty Tanuar - Bina Nusantara University, Jakarta,11480, Indonesia
Johan Muliadi Kerta - Bina Nusantara University, Jakarta,11480, Indonesia

Citation Format:

DOI: http://dx.doi.org/10.30630/joiv.7.4.01359


Several cities in Indonesia, such as Cirebon, Bandung, and Bogor, have several historical buildings that date back to the Dutch colonial period. Several Dutch colonial heritage buildings can be found in several areas. The existence of historical buildings also would attract foreign or local tourists who visit one of an area. We need a technology or model that would support the recognition and identification of buildings, including their characteristics. However, recognizing and identifying them is a problem in itself, so technology would be needed to help them. The technology or model that would be implemented in this research is the Convolutional Neural Network model, a derivative of Artificial Intelligent technology focused on image processing and pattern recognition. This process consists of several stages. The initial stage uses the Gaussian Blur, SuCK, and CLAHE methods which are useful for image sharpening and recognition. The second process is feature extraction of the image characteristics of buildings. The results of the image process will support the third process, namely the image retrieval process of buildings based on their characteristics. Based on these three main processes, they would facilitate and support local and foreign tourists to recognize historic buildings in the area. In this experiment, the Euclidean distance and Manhattan distance methods were used in the retrieval process. The highest accuracy in the retrieval process for the feature extraction process with the DenseNet 121 model with the initial process is Gaussian Blur of 88.96% and 88.46%, with the SuCK method of 88.3 and 87.8%, and with CLAHE of 87.7%, and 87.6%. We hope that this research can be continued to identify buildings with more complex characteristics and models.


Historical building; SuCK; CLAHE; gaussian blur; convolutional neural network; artificial intelligent

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