Automatic Image Annotation Using Adaptive Convolutional Deep Learning Model
作者机构:Department of Computer Science and EngineeringHindusthan College of Engineering and TechnologyCoimbatore641032TamilnaduIndia Department of Computer Science and EngineeringHindusthan Institute of TechnologyCoimbatore641032TamilnaduIndia
出 版 物:《Intelligent Automation & Soft Computing》 (智能自动化与软计算(英文))
年 卷 期:2023年第36卷第4期
页 面:481-497页
核心收录:
学科分类:08[工学] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术(可授工学、理学学位)]
主 题:Deep learning model J-image segmentation honey badger algorithm convolutional neural network image annotation
摘 要:Every day,websites and personal archives create more and more *** size of these archives is *** comfort of use of these huge digital image gatherings donates to their ***,not all of these folders deliver relevant indexing *** the outcomes,it is dif-ficult to discover data that the user can be absorbed ***,in order to determine the significance of the data,it is important to identify the contents in an informative *** annotation can be one of the greatest problematic domains in multimedia research and computer ***,in this paper,Adap-tive Convolutional Deep Learning Model(ACDLM)is developed for automatic image ***,the databases are collected from the open-source system which consists of some labelled images(for training phase)and some unlabeled images{Corel 5 K,MSRC v2}.After that,the images are sent to the pre-processing step such as colour space quantization and texture color class *** pre-processed images are sent to the segmentation approach for efficient labelling technique using J-image segmentation(JSEG).Thefinal step is an auto-matic annotation using ACDLM which is a combination of Convolutional Neural Network(CNN)and Honey Badger Algorithm(HBA).Based on the proposed classifier,the unlabeled images are *** proposed methodology is imple-mented in MATLAB and performance is evaluated by performance metrics such as accuracy,precision,recall and F1_*** the assistance of the pro-posed methodology,the unlabeled images are labelled.