Features Conduction Neural Response and Its Application in Content-Based Image Retrieval
Joint Authors
Yue, Tian
Hu, Zhengfa
Xiao, Haixia
Source
Mathematical Problems in Engineering
Issue
Vol. 2016, Issue 2016 (31 Dec. 2016), pp.1-12, 12 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2016-09-29
Country of Publication
Egypt
No. of Pages
12
Main Subjects
Abstract EN
A novel image representation is proposed for content-based image retrieval (CBIR).
The core idea of the proposed method is to do deep learning for the local features of image and to melt semantic component into the representation through a hierarchical architecture which is built to simulate human visual perception system, and then a new image descriptor of features conduction neural response (FCNR) is constructed.
Compared with the classical neural response (NR), FCNR has lower computational complexity and is more suitable for CBIR tasks.
The results of experiments on a commonly used image database demonstrate that, compared with those of NR related methods or some other image descriptors that were originally developed for CBIR, the proposed method has wonderful performance on retrieval efficiency and effectiveness.
American Psychological Association (APA)
Hu, Zhengfa& Yue, Tian& Xiao, Haixia. 2016. Features Conduction Neural Response and Its Application in Content-Based Image Retrieval. Mathematical Problems in Engineering،Vol. 2016, no. 2016, pp.1-12.
https://search.emarefa.net/detail/BIM-1112110
Modern Language Association (MLA)
Hu, Zhengfa…[et al.]. Features Conduction Neural Response and Its Application in Content-Based Image Retrieval. Mathematical Problems in Engineering No. 2016 (2016), pp.1-12.
https://search.emarefa.net/detail/BIM-1112110
American Medical Association (AMA)
Hu, Zhengfa& Yue, Tian& Xiao, Haixia. Features Conduction Neural Response and Its Application in Content-Based Image Retrieval. Mathematical Problems in Engineering. 2016. Vol. 2016, no. 2016, pp.1-12.
https://search.emarefa.net/detail/BIM-1112110
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1112110