Department of Computer Science and Engineering, GITAM (Deemed to be University), Hyderabad 502329, Telangana, India.
Department of Computer Science and Engineering, SRM University, Guntur 522302, Andhra Pradesh, India.
Comput Intell Neurosci. 2023 Feb 23;2023:6257573. doi: 10.1155/2023/6257573. eCollection 2023.
Digital data are rising fast as Internet technology advances through many sources, such as smart phones, social networking sites, IoT, and other communication channels. Therefore, successfully storing, searching, and retrieving desired images from such large-scale databases are critical. Low-dimensional feature descriptors play an essential role in speeding up the retrieval process in such a large-scale dataset. A feature extraction approach based on the integration of color and texture contents has been proposed in the proposed system for the construction of a low-dimensional feature descriptor. In which color contents are quantified from a preprocessed quantized HSV color image and texture contents are retrieved from a Sobel edge detection-based preprocessed V-plane of HSV color image using a block level DCT (discrete cosine transformation) and gray level co-occurrence matrix. On a benchmark image dataset, the suggested image retrieval scheme is validated. The experimental outcomes were compared to ten cutting-edge image retrieval algorithms, which outperformed in the vast majority of cases.
随着互联网技术的发展，数字数据迅速增长，来源众多，如智能手机、社交网站、物联网和其他通信渠道。因此，成功地从这些大规模数据库中存储、搜索和检索所需的图像至关重要。在这样的大规模数据集中，低维特征描述符在加快检索过程中起着至关重要的作用。所提出的系统提出了一种基于颜色和纹理内容集成的特征提取方法，用于构建低维特征描述符。其中，颜色内容从预处理的量化 HSV 颜色图像中量化，纹理内容从基于 Sobel 边缘检测的 HSV 颜色图像的 V 平面预处理中检索，使用块级 DCT（离散余弦变换）和灰度共生矩阵。在基准图像数据集上验证了所提出的图像检索方案。实验结果与十种最先进的图像检索算法进行了比较，在大多数情况下都表现出色。
