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Content-Based Image Retrieval Using DNA Transcription and Translation.

Jitesh Pradhan, Chiranjeev Bhaya, Arup Kumar Pal

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    Summary

    This study introduces a novel Content-Based Image Retrieval (CBIR) technique using DNA sequences to store image data. This DNA-based computing approach enhances classifier efficiency for improved image retrieval performance.

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    Area of Science:

    • Bioinformatics
    • Computer Science
    • Genetics

    Background:

    • DNA is fundamental for storing and transmitting genetic information in living organisms.
    • Genetic information flows through transcription and translation, converting DNA into amino-acid sequences that define phenotypes.
    • Traditional image retrieval methods face challenges with high-dimensional feature vectors.

    Purpose of the Study:

    • To develop a novel Content-Based Image Retrieval (CBIR) technique leveraging DNA-based computing.
    • To reduce feature-vector dimensions while preserving essential image information.
    • To enhance classifier efficiency and overall image retrieval performance.

    Main Methods:

    • Images are encoded into DNA sequences.
    • Amino-acid sequences are extracted from DNA sequences to form feature vectors.
    • Ensemble learning is applied to classifiers trained and tested with these feature vectors.

    Main Results:

    • The proposed method significantly reduces feature-vector dimensions.
    • Essential image information is effectively preserved during the DNA encoding process.
    • Experimental results demonstrate superior efficiency compared to existing state-of-the-art algorithms.

    Conclusions:

    • DNA-based computing offers an efficient approach for enhancing CBIR systems.
    • The novel technique improves classifier performance and image retrieval accuracy.
    • This method represents a significant advancement in leveraging biological computing principles for data retrieval.