Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/67191
Title: M-SBIR : An improved sketch-based image retrieval method using visual word mapping
Authors: Niu, J
Ma, J
Lu, J
Liu, X
Zhu, Z
Keywords: Co-segmentation
M-SBIR
Mapping
SBIR
Visual word
Issue Date: 2017
Publisher: Springer
Source: Lecture notes in computer science (including subseries Lecture notes in artificial intelligence and lecture notes in bioinformatics), 2017, v. 10133, p. 257-268 How to cite?
Journal: Lecture notes in computer science (including subseries Lecture notes in artificial intelligence and lecture notes in bioinformatics) 
Abstract: Sketch-based image retrieval (SBIR) systems, which interactively search photo collections using free-hand sketches depicting shapes, have attracted much attention recently. In most existing SBIR techniques, the color images stored in a database are first transformed into corresponding sketches. Then, features of the sketches are extracted to generate the sketch visual words for later retrieval. However, transforming color images to sketches will normally incur loss of information, thus decreasing the final performance of SBIR methods. To address this problem, we propose a new method called M-SBIR. In M-SBIR, besides sketch visual words, we also generate a set of visual words from the original color images. Then, we leverage the mapping between the two sets to identify and remove sketch visual words that cannot describe the original color images well. We demonstrate the performance of M-SBIR on a public data set. We show that depending on the number of different visual words adopted, our method can achieve 9.8 ∼ 13.6% performance improvement compared to the classic SBIR techniques. In addition, we show that for a database containing multiple color images of the same objects, the performance of M-SBIR can be further improved via some simple techniques like co-segmentation.
Description: 23rd International Conference on Multimedia Modeling, MMM 2017, Iceland, 4-6 January 2017
URI: http://hdl.handle.net/10397/67191
ISBN: 9783319518138
ISSN: 0302-9743
EISSN: 1611-3349
DOI: 10.1007/978-3-319-51814-5_22
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