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Noise Resilient Local Gradient Orientation for Content-Based Image Retrieval
International Journal of Optics ( IF 1.8 ) Pub Date : 2021-07-14 , DOI: 10.1155/2021/4151482
Samina Bilquees 1 , Hassan Dawood 1 , Hussain Dawood 2 , Nadeem Majeed 3 , Ali Javed 4 , Muhammad Tariq Mahmood 5
Affiliation  

In a world of multimedia information, where users seek accurate results against search query and demand relevant multimedia content retrieval, developing an accurate content-based image retrieval (CBIR) system is difficult due to the presence of noise in the image. The performance of the CBIR system is impaired by this noise. To estimate the distance between the query and database images, CBIR systems use image feature representation. The noise or artifacts present within the visual data might confuse the CBIR when retrieving relevant results. Therefore, we propose Noise Resilient Local Gradient Orientation (NRLGO) feature representation that overcomes the noise factor within the visual information and strengthens the CBIR to retrieve accurate and relevant results. The proposed NRLGO consists of three steps: estimation and removal of noise to protect the local visual structure; extraction of color, texture, and local contrast features; and, at the end, generation of microstructure for visual representation. The Manhattan distance between the query image and the database image is used to measure their similarity. The proposed technique was tested using the Corel dataset, which contains 10000 images from 100 different categories. The outcomes of the experiment signify that the proposed NRLGO has higher retrieval performance in comparison with state-of-the-art techniques.

中文翻译:

用于基于内容的图像检索的抗噪局部梯度方向

在多媒体信息的世界中,用户针对搜索查询寻求准确的结果并要求进行相关的多媒体内容检索,由于图像中存在噪声,因此难以开发准确的基于内容的图像检索 (CBIR) 系统。CBIR 系统的性能受到这种噪声的影响。为了估计查询图像和数据库图像之间的距离,CBIR 系统使用图像特征表示。在检索相关结果时,视觉数据中存在的噪声或伪影可能会混淆 CBIR。因此,我们提出了噪声弹性局部梯度定向(NRLGO)特征表示,它克服了视觉信息中的噪声因素并加强了 CBIR 以检索准确和相关的结果。提议的 NRLGO 包括三个步骤:估计和去除噪声以保护局部视觉结构;颜色、纹理和局部对比度特征的提取;最后,生成用于视觉表示的微观结构。查询图像和数据库图像之间的曼哈顿距离用于衡量它们的相似性。所提出的技术使用 Corel 数据集进行了测试,该数据集包含来自 100 个不同类别的 10000 张图像。实验结果表明,与最先进的技术相比,所提出的 NRLGO 具有更高的检索性能。所提出的技术使用 Corel 数据集进行了测试,该数据集包含来自 100 个不同类别的 10000 张图像。实验结果表明,与最先进的技术相比,所提出的 NRLGO 具有更高的检索性能。所提出的技术使用 Corel 数据集进行了测试,该数据集包含来自 100 个不同类别的 10000 张图像。实验结果表明,与最先进的技术相比,所提出的 NRLGO 具有更高的检索性能。
更新日期:2021-07-14
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