Fine-Grained Color Sketch-Based Image Retrieval

This source preferred by Lihua You

Authors: Xia, Y., Wang, S., Li, Y., You, L., Yang, X. and Zhang, J.J.

http://eprints.bournemouth.ac.uk/32502/

Start date: 17 June 2019

Journal: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volume: 11542 LNCS

Pages: 424-430

eISSN: 1611-3349

ISBN: 9783030225131

ISSN: 0302-9743

DOI: 10.1007/978-3-030-22514-8_40

© Springer Nature Switzerland AG 2019. We propose a novel fine-grained color sketch-based image retrieval (CSBIR) approach. The CSBIR problem is investigated for the first time using deep learning networks, in which deep features are used to represent color sketches and images. A novel ranking method considering both shape matching and color matching is also proposed. In addition, we build a CSBIR dataset with color sketches and images to train and test our method. The results show that our method has better retrieval performance.

This data was imported from Scopus:

Authors: Xia, Y., Wang, S., Li, Y., You, L., Yang, X. and Zhang, J.J.

http://eprints.bournemouth.ac.uk/32502/

Journal: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volume: 11542 LNCS

Pages: 424-430

eISSN: 1611-3349

ISBN: 9783030225131

ISSN: 0302-9743

DOI: 10.1007/978-3-030-22514-8_40

© Springer Nature Switzerland AG 2019. We propose a novel fine-grained color sketch-based image retrieval (CSBIR) approach. The CSBIR problem is investigated for the first time using deep learning networks, in which deep features are used to represent color sketches and images. A novel ranking method considering both shape matching and color matching is also proposed. In addition, we build a CSBIR dataset with color sketches and images to train and test our method. The results show that our method has better retrieval performance.

This data was imported from Web of Science (Lite):

Authors: Xia, Y., Wang, S., Li, Y., You, L., Yang, X. and Zhang, J.J.

http://eprints.bournemouth.ac.uk/32502/

Journal: ADVANCES IN COMPUTER GRAPHICS, CGI 2019

Volume: 11542

Pages: 424-430

eISSN: 1611-3349

ISBN: 978-3-030-22513-1

ISSN: 0302-9743

DOI: 10.1007/978-3-030-22514-8_40

The data on this page was last updated at 05:24 on October 27, 2020.