Ancient Buildings Identification Using Deep Learning Algorithm and Similarity Distance
Keywords:
ancient buildings, SuCK, CLAHE, DenseNet121, Gaussian Blur, Euclidean Distance, Manhattan DistenceAbstract
Several cities in Indonesia such as Bandung, Cirebon and Bogor have many ancient buildings remaining, especially since the Dutch colonial period. However, identifying ancient buildings is a problem because people need to understand the existence of ancient buildings. A technology is needed to support the identification of ancient buildings, including their characteristics. The technology used is Artificial Intelligent which focuses on image processing and pattern recognition. This recognition process consists of Preprocess, Feature Extraction, and Building Image Classification. The Gaussian Blur method was used for the preprocessing, Sharpening used the Convolutional Kernel (SuCK) and Contrast Limited adaptive histogram equalization (CLAHE). All preprocessing is used to support feature extraction and image retrieval processes. This experiment uses several CNN models that perform feature extraction while the retrieval process uses Euclidean and Manhattan distances. Based on the results of the highest accuracy experiment used the DenseNet 121 model, where Initial process used Gaussian Blur, and the similarity distance with the Euclidean distance is 88.96% and 88.46% with the Manhattan Distance. For the initial process used SuCK method and the similarity with the Euclidean Distance is 88.26% and 87.81% with the Manhattan Distance. For the initial process used CLAHE and the similarity distance with the Euclidean Distance is 87.68% and 87.61% with the Manhattan Distance This research can be continued to identify ancient buildings with more complex characteristics and models.
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