Content-Based Image Retrieval Using Colour, Gray, Advanced Texture, Shape Features, and Random Forest Classifier with Optimized Particle Swarm Optimization

IF 3.3 Q2 ENGINEERING, BIOMEDICAL
Dr. MANOHARAN SUBRAMANIAN, Velmurugan Lingamuthu, Chandran Venkatesan, S. Perumal
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引用次数: 4

Abstract

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.
基于颜色、灰度、高级纹理、形状特征和具有优化粒子群优化的随机森林分类器的内容图像检索
本文提出了一种基于内容的图像检索(CBIR)的新方法,即提取输入查询图像的颜色、灰度、高级纹理和形状特征。采用基于轮廓的形状特征提取方法和图像矩提取技术提取形状特征和形状不变特征。利用粒子群算法从提取的特征中选择信息特征,并结合颜色、灰度、纹理和形状特征。通过训练随机森林分类器,对给定的查询图像检索到目标图像。提出的颜色、灰度、高级纹理、形状特征和随机森林分类器与优化的粒子群算法(CGATSFRFOPSO)提供了大规模数据库中图像的高效检索。本研究的主要目的是通过从数据库图像和查询图像中提取颜色、灰度、纹理、形状等特征,提高CBIR系统的效率和有效性。对提取的特征进行最优特征选择去除冗余和最优加权线性组合融合等不同层次的处理。采用粒子群算法从灰度、颜色和纹理特征中选择信息特征。通过对相似度搜索的机器学习算法的集成,提高了匹配精度和图像检索速度。
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来源期刊
CiteScore
12.00
自引率
0.00%
发文量
11
审稿时长
20 weeks
期刊介绍: The International Journal of Biomedical Imaging is managed by a board of editors comprising internationally renowned active researchers. The journal is freely accessible online and also offered for purchase in print format. It employs a web-based review system to ensure swift turnaround times while maintaining high standards. In addition to regular issues, special issues are organized by guest editors. The subject areas covered include (but are not limited to): Digital radiography and tomosynthesis X-ray computed tomography (CT) Magnetic resonance imaging (MRI) Single photon emission computed tomography (SPECT) Positron emission tomography (PET) Ultrasound imaging Diffuse optical tomography, coherence, fluorescence, bioluminescence tomography, impedance tomography Neutron imaging for biomedical applications Magnetic and optical spectroscopy, and optical biopsy Optical, electron, scanning tunneling/atomic force microscopy Small animal imaging Functional, cellular, and molecular imaging Imaging assays for screening and molecular analysis Microarray image analysis and bioinformatics Emerging biomedical imaging techniques Imaging modality fusion Biomedical imaging instrumentation Biomedical image processing, pattern recognition, and analysis Biomedical image visualization, compression, transmission, and storage Imaging and modeling related to systems biology and systems biomedicine Applied mathematics, applied physics, and chemistry related to biomedical imaging Grid-enabling technology for biomedical imaging and informatics
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