Fine-Grained Color Sketch-Based Image Retrieval

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

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

Abstract:

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.

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

Source: Scopus

Fine-Grained Color Sketch-Based Image Retrieval

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

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

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

Source: Web of Science (Lite)

Fine-Grained Color Sketch-Based Image Retrieval

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

Conference: CGI 2019

Dates: 17-20 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

Abstract:

© 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.

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

Source: Manual

Preferred by: Lihua You

Fine-Grained Color Sketch-Based Image Retrieval

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

Conference: CGI 2019: Advances in Computer Graphics

Pages: 424-430

Abstract:

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.

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

Source: BURO EPrints