Rapid Feature Retrieval Method in Large-Scale Image Database

作     者：Gao, Fei 

作者机构：Tibet Univ Sch Informat Sci & Technol 36 Jiangsu Rd Lhasa 850000 Tibet Peoples R China 

出 版 物：《JOURNAL OF ADVANCED COMPUTATIONAL INTELLIGENCE AND INTELLIGENT INFORMATICS》 (J. Adv. Comput. Intell. Intelligent Informatics)

年 卷 期：2018年第22卷第7期

页      面：1088-1092页

核心收录：

学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：Key research and development and transformation plans of Tibet autonomous region [XZ201703-GC-09] 2016 youth teacher innovation support program project of universities in Tibet autonomous region [QC2016-13] 2016 Tibet autonomous region regional natural fund project [2016zr-tu-01] 

主　　题：image database feature classification rapid retrieval 

摘      要：The retrieval of features in a large-scale image database can improve the degree of visualization of images. The conventional method of feature-retrieval is a time-consuming process because it retrieves by searching the keywords. In this paper, a rapid feature retrieval method based on granular computing is proposed for use in a large-scale image database. In this method, we first collect and process the images from the database. Next, we construct a binary tree to realize the multi-class classification of the image features and complete the feature retrieval using support vector machines. The experimental results demonstrate that the proposed method can effectively retrieve the features in the large-scale image database. The effectiveness of retrieval can reach more than 95%.