Analysis of Colored Pottery Decoration using Hidden Markov Model Directional Clustering Classification with Deep Learning
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Abstract
Colored pottery decoration is an important cultural artifact that carries significant imagery, symbols, and cultural connotations. This paper presented an in-depth analysis of colored pottery decoration by employing a novel approach, Hidden Markov Model Directional Clustering Classification (HMMDCC), combined with deep learning techniques. The evaluated data comprehensive dataset of colored pottery designs, representing different historical periods and cultural contexts. The imagery, symbols, and cultural connotations embedded in the designs are extracted through a combination of computer vision and image processing techniques. The HMMDCC model is then utilized to perform directional clustering, which identifies spatial relationships and patterns within the decoration elements. To enhance classification accuracy and capture intricate patterns, deep learning techniques are incorporated into the HMMDCC model. The deep learning model is trained on the dataset, enabling it to recognize and classify the imagery, symbols, and cultural connotations present in colored pottery decoration. The findings of this study shed light on the hidden meanings and cultural significance associated with colored pottery decoration. The application of the HMMDCC model with deep learning showcases its effectiveness in analyzing and interpreting complex visual data. The results contribute to a deeper understanding of the historical and cultural contexts in which colored pottery decoration emerged, providing valuable insights for archaeologists, historians, and art enthusiasts.
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