Rapid Feature Retrieval Method in Large-Scale Image Database | Zendy

Fei Gao | Zendy

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Rapid Feature Retrieval Method in Large-Scale Image Database

Author(s) -

Fei Gao

Publication year - 2018

Publication title -

journal of advanced computational intelligence and intelligent informatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.172

H-Index - 20

eISSN - 1343-0130

pISSN - 1883-8014

DOI - 10.20965/jaciii.2018.p1088

Subject(s) - computer science , image retrieval , visual word , feature (linguistics) , process (computing) , automatic image annotation , scale (ratio) , pattern recognition (psychology) , artificial intelligence , image (mathematics) , visualization , construct (python library) , data mining , database , information retrieval , linguistics , philosophy , physics , quantum mechanics , programming language , operating system

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%.

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