Triplet Network-Based DNA Encoding for Enhanced Similarity Image Retrieval

With the exponential growth of digital data, DNA is emerging as an attractive medium for storage and computing. Thus, design methods for encoding, storing, and searching digital data within DNA storage are of utmost importance. This paper introduces image classification as a measurable task for evaluating the performance of DNA encoders in similar image searches. Furthermore, we propose a novel triplet network-based DNA encoder to improve the accuracy and efficiency. The evaluation using the CIFAR-100 dataset demonstrates that the proposed encoder outperforms existing encoders in retrieving similar images, with an accuracy of 0.77, which is equivalent to 94% of the practical upper limit, and 16 times faster training time.