Please use this identifier to cite or link to this item: https://hdl.handle.net/1959.11/26378
Title: A novel shape descriptor based on salient keypoints detection for binary image matching and retrieval
Contributor(s): Chatbri, Houssem (author); Kameyama, Keisuke (author); Kwan, Paul  (author); Little, Suzanne (author); O'Connor, Noel E (author)
Publication Date: 2018-11
Early Online Version: 2018-05-10
DOI: 10.1007/s11042-018-6054-x
Handle Link: https://hdl.handle.net/1959.11/26378
Abstract: We introduce a shape descriptor that extracts keypoints from binary images and automatically detects the salient ones among them. The proposed descriptor operates as follows: First, the contours of the image are detected and an image transformation is used to generate background information. Next, pixels of the transformed image that have specific characteristics in their local areas are used to extract keypoints. Afterwards, the most salient keypoints are automatically detected by filtering out redundant and sensitive ones. Finally, a feature vector is calculated for each keypoint by using the distribution of contour points in its local area. The proposed descriptor is evaluated using public datasets of silhouette images, handwritten math expressions, hand-drawn diagram sketches, and noisy scanned logos. Experimental results show that the proposed descriptor compares strongly against state of the art methods, and that it is reliable when applied on challenging images such as fluctuated handwriting and noisy scanned images. Furthermore, we integrate our descriptor in a content-based document image retrieval system using sketch queries as a step for query and candidate occurrence matching, and we show that it leads to a significant boost in retrieval performances.
Publication Type: Journal Article
Source of Publication: Multimedia Tools and Applications, 77(21), p. 28925-28948
Publisher: Springer New York LLC
Place of Publication: United States of America
ISSN: 1573-7721
1380-7501
Fields of Research (FoR) 2008: 080109 Pattern Recognition and Data Mining
080305 Multimedia Programming
080106 Image Processing
Fields of Research (FoR) 2020: 460306 Image processing
Socio-Economic Objective (SEO) 2008: 890201 Application Software Packages (excl. Computer Games)
890301 Electronic Information Storage and Retrieval Services
970108 Expanding Knowledge in the Information and Computing Sciences
Socio-Economic Objective (SEO) 2020: 220401 Application software packages
220302 Electronic information storage and retrieval services
280115 Expanding knowledge in the information and computing sciences
Peer Reviewed: Yes
HERDC Category Description: C1 Refereed Article in a Scholarly Journal
Appears in Collections:Journal Article
School of Science and Technology

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