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 Name: DICTA 2013: International Conference on Digital Image Computing: Techniques and Applications, Hobart, Australia, 26th - 28th November, 2013
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: IEEE: Institute of Electrical and Electronics Engineers
Place of Publication: New York, United States of America
Field of Research (FOR): 080104 Computer Vision
080109 Pattern Recognition and Data Mining
080106 Image Processing
Socio-Economic Outcome Codes: 970110 Expanding Knowledge in Technology
970108 Expanding Knowledge in the Information and Computing Sciences
890201 Application Software Packages (excl. Computer Games)
Peer Reviewed: Yes
HERDC Category Description: E1 Refereed Scholarly Conference Publication
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