Batik Image Retrieval using Convolutional Autoencoder

Image retrieval systems have long been a cornerstone of multimedia databases, aiming to extract relevant images from vast datasets. Batik, an intricate traditional art form originating from Indonesia, presents unique challenges in this domain. Its diverse patterns and rich history have led to a broad array of designs, making the retrieval of specific Batik images a formidable task. This research paper introduces a novel approach that employs a convolutional auto encoder to address the challenges of Batik image retrieval. The underlying problem we address is the difficulty in obtaining high precision results from conventional image retrieval systems when dealing with the detailed patterns of Batik. Our methodology hinges on a convolutional autoencoder, a deep learning model adept at extracting pivotal features from images and using them for various tasks, including image retrieval. The effectiveness of our method was evaluated using the Batik Nitik dataset, a comprehensive collection of 960 images representing a wide range of Batik designs. The outcomes were promising. Our proposed method achieved a remarkable precision of 0.98, a testament to its capability in accurately retrieving relevant Batik images. Furthermore, this research not only demonstrates the potential of convolutional autoencoders in the realm of image retrieval but also offers a solution tailored to the unique intricacies of Batik. In conclusion, the convolutional autoencoder presents a groundbreaking approach to Batik image retrieval, merging traditional art with modern deep learning techniques to ensure accuracy and relevance in results. © 2024 IEEE.