DNA-CBIR: DNA Translation Inspired Codon Pattern-based Deep Image Feature Extraction for Content-based Image Retrieval

DNA is emerging as a promising medium for storing huge volumes of data in a confined space that remains intact for thousands of years. Although this technique is very efficient, especially for multimedia data like images, there is a lack of efficient searching and retrieval technique. This paper addresses this issue and proposes a novel Content Based Image Retrieval (CBIR) technique to retrieve similar images from the generated DNA-based image feature vectors. The features are obtained by a novel encoding scheme that uses the three Most-Significant Bits from the images and converts them into a string of nucleotides that follow run length and GC constraints to form DNA planes stored in a DNA medium. The nucleotides in these planes are interpreted through three consecutive sequences forming codons. The codon-based features are then utilized to perform instance-based image retrieval. The DNA planes are further adapted and implemented on diverse deep learning architectures, including ResNet-50, VGG-16, VGG-19, and Inception V3, to facilitate classification-based image retrieval tasks. The system’s performance has been assessed using a range of datasets, encompassing coral, medical, and multi-label images. Experimental results demonstrate that the proposed approach achieves notable improvements when compared to existing state-of-the-art methods.