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
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.
https://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
https://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.
https://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.
https://eprints.bournemouth.ac.uk/32502/
Source: BURO EPrints