{"title":"基于 DNA 编码的核苷酸模式和深度特征,用于基于实例和类别的图像检索。","authors":"Jitesh Pradhan;Arup Kumar Pal;Sk Hafizul Islam;Chiranjeev Bhaya","doi":"10.1109/TNB.2023.3303512","DOIUrl":null,"url":null,"abstract":"Recently, DNA encoding has shown its potential to store the vital information of the image in the form of nucleotides, namely \n<inline-formula> <tex-math>${A}, {C}, {T}$ </tex-math></inline-formula>\n, and \n<inline-formula> <tex-math>${G}$ </tex-math></inline-formula>\n, with the entire sequence following run-length and GC-constraint. As a result, the encoded DNA planes contain unique nucleotide strings, giving more salient image information using less storage. In this paper, the advantages of DNA encoding have been inherited to uplift the retrieval accuracy of the content-based image retrieval (CBIR) system. Initially, the most significant bit-plane-based DNA encoding scheme has been suggested to generate DNA planes from a given image. The generated DNA planes of the image efficiently capture the salient visual information in a compact form. Subsequently, the encoded DNA planes have been utilized for nucleotide patterns-based feature extraction and image retrieval. Simultaneously, the translated and amplified encoded DNA planes have also been deployed on different deep learning architectures like ResNet-50, VGG-16, VGG-19, and Inception V3 to perform classification-based image retrieval. The performance of the proposed system has been evaluated using two corals, an object, and a medical image dataset. All these datasets contain 28,200 images belonging to 134 different classes. The experimental results confirm that the proposed scheme achieves perceptible improvements compared with other state-of-the-art methods.","PeriodicalId":13264,"journal":{"name":"IEEE Transactions on NanoBioscience","volume":null,"pages":null},"PeriodicalIF":3.7000,"publicationDate":"2023-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"DNA Encoding-Based Nucleotide Pattern and Deep Features for Instance and Class-Based Image Retrieval\",\"authors\":\"Jitesh Pradhan;Arup Kumar Pal;Sk Hafizul Islam;Chiranjeev Bhaya\",\"doi\":\"10.1109/TNB.2023.3303512\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Recently, DNA encoding has shown its potential to store the vital information of the image in the form of nucleotides, namely \\n<inline-formula> <tex-math>${A}, {C}, {T}$ </tex-math></inline-formula>\\n, and \\n<inline-formula> <tex-math>${G}$ </tex-math></inline-formula>\\n, with the entire sequence following run-length and GC-constraint. As a result, the encoded DNA planes contain unique nucleotide strings, giving more salient image information using less storage. In this paper, the advantages of DNA encoding have been inherited to uplift the retrieval accuracy of the content-based image retrieval (CBIR) system. Initially, the most significant bit-plane-based DNA encoding scheme has been suggested to generate DNA planes from a given image. The generated DNA planes of the image efficiently capture the salient visual information in a compact form. Subsequently, the encoded DNA planes have been utilized for nucleotide patterns-based feature extraction and image retrieval. Simultaneously, the translated and amplified encoded DNA planes have also been deployed on different deep learning architectures like ResNet-50, VGG-16, VGG-19, and Inception V3 to perform classification-based image retrieval. The performance of the proposed system has been evaluated using two corals, an object, and a medical image dataset. All these datasets contain 28,200 images belonging to 134 different classes. 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引用次数: 0
摘要
最近,DNA 编码显示了其潜力,它能以核苷酸(即 A、C、T 和 G)的形式存储图像的重要信息,整个序列遵循运行长度和 GC 限制。因此,编码后的 DNA 平面包含独特的核苷酸字符串,用更少的存储空间提供更多突出的图像信息。本文继承了 DNA 编码的优点,以提高基于内容的图像检索(CBIR)系统的检索精度。最初,我们提出了一种最重要的基于位平面的 DNA 编码方案,用于从给定图像生成 DNA 平面。生成的图像 DNA 平面能以紧凑的形式有效捕捉到突出的视觉信息。随后,编码后的 DNA 平面被用于基于核苷酸模式的特征提取和图像检索。同时,经过翻译和放大的编码 DNA 平面还被部署在不同的深度学习架构上,如 ResNet-50、VGG-16、VGG-19 和 Inception V3,以执行基于分类的图像检索。我们使用两个珊瑚、一个物体和一个医学图像数据集对所提议系统的性能进行了评估。所有这些数据集包含属于 134 个不同类别的 28 200 张图像。实验结果证实,与其他最先进的方法相比,所提出的方案实现了可感知的改进。
DNA Encoding-Based Nucleotide Pattern and Deep Features for Instance and Class-Based Image Retrieval
Recently, DNA encoding has shown its potential to store the vital information of the image in the form of nucleotides, namely
${A}, {C}, {T}$
, and
${G}$
, with the entire sequence following run-length and GC-constraint. As a result, the encoded DNA planes contain unique nucleotide strings, giving more salient image information using less storage. In this paper, the advantages of DNA encoding have been inherited to uplift the retrieval accuracy of the content-based image retrieval (CBIR) system. Initially, the most significant bit-plane-based DNA encoding scheme has been suggested to generate DNA planes from a given image. The generated DNA planes of the image efficiently capture the salient visual information in a compact form. Subsequently, the encoded DNA planes have been utilized for nucleotide patterns-based feature extraction and image retrieval. Simultaneously, the translated and amplified encoded DNA planes have also been deployed on different deep learning architectures like ResNet-50, VGG-16, VGG-19, and Inception V3 to perform classification-based image retrieval. The performance of the proposed system has been evaluated using two corals, an object, and a medical image dataset. All these datasets contain 28,200 images belonging to 134 different classes. The experimental results confirm that the proposed scheme achieves perceptible improvements compared with other state-of-the-art methods.
期刊介绍:
The IEEE Transactions on NanoBioscience reports on original, innovative and interdisciplinary work on all aspects of molecular systems, cellular systems, and tissues (including molecular electronics). Topics covered in the journal focus on a broad spectrum of aspects, both on foundations and on applications. Specifically, methods and techniques, experimental aspects, design and implementation, instrumentation and laboratory equipment, clinical aspects, hardware and software data acquisition and analysis and computer based modelling are covered (based on traditional or high performance computing - parallel computers or computer networks).