Please use this identifier to cite or link to this item:
https://hdl.handle.net/1959.11/13853
Title: | Sketch-Based Image Retrieval By Size-Adaptive and Noise-Robust Feature Description | Contributor(s): | Chatbri, Houssem (author); Kameyama, Keisuke (author); Kwan, Paul H (author) | Publication Date: | 2013 | DOI: | 10.1109/DICTA.2013.6691528 | Handle Link: | https://hdl.handle.net/1959.11/13853 | Abstract: | We review available methods for Sketch-Based Image Retrieval (SBIR) and we discuss their limitations. Then, we present two SBIR algorithms: The first algorithm extracts shape features by using support regions calculated for each sketch point, and the second algorithm adapts the Shape Context descriptor [1] to make it scale invariant and enhances its performance in presence of noise. Both algorithms share the property of calculating the feature extraction window according to the sketch size. Experiments and comparative evaluation with state-of-the-art methods show that the proposed algorithms are competitive in distinctiveness capability and robust against noise. | Publication Type: | Conference Publication | Conference Details: | DICTA 2013: International Conference on Digital Image Computing: Techniques and Applications, Hobart, Australia, 26th - 28th November, 2013 | Source of Publication: | Proceedings of the 2013 International Conference on Digital Image Computing: Techniques and Applications (DICTA), p. 469-476 | Publisher: | Institute of Electrical and Electronics Engineers (IEEE) | Place of Publication: | Los Alamitos, United States of America | Fields of Research (FoR) 2008: | 080104 Computer Vision 080109 Pattern Recognition and Data Mining 080106 Image Processing |
Fields of Research (FoR) 2020: | 460301 Active sensing 461199 Machine learning not elsewhere classified 460306 Image processing |
Socio-Economic Objective (SEO) 2008: | 970110 Expanding Knowledge in Technology 970108 Expanding Knowledge in the Information and Computing Sciences 890201 Application Software Packages (excl. Computer Games) |
Socio-Economic Objective (SEO) 2020: | 280115 Expanding knowledge in the information and computing sciences 220401 Application software packages |
Peer Reviewed: | Yes | HERDC Category Description: | E1 Refereed Scholarly Conference Publication |
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Appears in Collections: | Conference Publication |
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