Subramanian Manoharan, Lingamuthu Velmurugan, Venkatesan Chandran, Perumal Sasikumar
Department of Computer Science, School of Informatics and Electrical Engineering, Institute of Technology, Hachalu Hundessa Campus, Ambo University, Ambo, Post Box No.: 19, Ethiopia.
Dr. N.G.P. Institute of Technology, Coimbatore-641407, Tamilnadu, India.
Int J Biomed Imaging. 2022 Apr 21;2022:3211793. doi: 10.1155/2022/3211793. eCollection 2022.
In this paper, a new approach for Content-Based Image Retrieval (CBIR) has been addressed by extracting colour, gray, advanced texture, and shape features for input query images. Contour-based shape feature extraction methods and image moment extraction techniques are used to extract the shape features and shape invariant features. The informative features are selected from extracted features and combined colour, gray, texture, and shape features by using PSO. The target image has been retrieved for the given query image by training the random forest classifier. The proposed colour, gray, advanced texture, shape feature, and random forest classifier with optimized PSO (CGATSFRFOPSO) provide efficient retrieval of images in a large-scale database. The main objective of this research work is to improve the efficiency and effectiveness of the CBIR system by extracting the features like colour, gray, texture, and shape from database images and query images. These extracted features are processed in various levels like removing redundancy by optimal feature selection and fusion by optimal weighted linear combination. The Particle Swarm Optimization algorithm is used for selecting the informative features from gray and colour and texture features. The matching accuracy and the speed of image retrieval are improved by an ensemble of machine learning algorithms for the similarity search.
本文提出了一种基于内容的图像检索（CBIR）新方法，通过提取输入查询图像的颜色、灰度、高级纹理和形状特征来实现。基于轮廓的形状特征提取方法和图像矩提取技术被用于提取形状特征和形状不变特征。从提取的特征中选择信息特征，并使用粒子群优化算法（PSO）组合颜色、灰度、纹理和形状特征。通过训练随机森林分类器，为给定的查询图像检索目标图像。所提出的颜色、灰度、高级纹理、形状特征以及带有优化PSO的随机森林分类器（CGATSFRFOPSO）能够在大规模数据库中高效检索图像。本研究工作的主要目标是通过从数据库图像和查询图像中提取颜色、灰度、纹理和形状等特征，提高CBIR系统的效率和有效性。这些提取的特征经过多个处理阶段，如通过最优特征选择去除冗余，以及通过最优加权线性组合进行融合。粒子群优化算法用于从灰度、颜色和纹理特征中选择信息特征。通过机器学习算法的集成进行相似性搜索，提高了图像检索的匹配精度和速度。
