DNA Transcription and Translation Inspired Deep Features for Classification-Based CBIR

DNA is a macromolecule that carries the genetic information of nearly all living things on the planet. They not only determine the characteristics and behavior of an organism but also pass the essential features to the next generation, ensuring that “like begets like." Because of their same genetic structure, organisms of the same species appear identical. Inspired by this property, a novel DNA-based scheme for class-based image retrieval has been proposed. The algorithm imitates the flow of genetic information, which is initially stored in the DNA and is transcripted and translated to RNA and amino-acid sequences, respectively, using genetic coding. Since similar images would generate a similar sequence, ensuring the preservation of salient features of the images required for retrieval. Thus, these amino-acid sequences are then deployed on a DNA-inspired ResNet-50 CNN architecture for performing image classification-based image retrieval. The proposed scheme has been extensively tested on six different datasets to demonstrate its performance. Comparative results reveal that the proposed scheme outperforms competing state-of-the-art Content-based Image Retrieval techniques in terms of retrieval performance.