A study of shape-based image retrieval

• Published in: 24th International Conference on Distributed Computing Systems Workshops (ICDCS 2004 Workshops) pp. 118 - 123
• Main Authors: Hwei-Jen Lin, Yang-Ta Kao, Shwu-Huey Yen, Chia-Jen Wang
• Format: Conference Proceeding
• Language: English
• Published: IEEE 2004
• Subjects: Active contours; Computer vision; Content based retrieval; Data mining; Image edge detection; Image retrieval; Information retrieval; Noise shaping; Robustness; Shape
• ISBN: 9780769520872; 0769520871
• DOI: 10.1109/ICDCSW.2004.1284018

Content-based image retrieval (CBIR) work includes feature selection, object representation, and matching. If a shape is used as feature, edge detection might be the first step to extract that feature. Invariance to translation, rotation, and scale is required by a good shape representation. Sustaining deformation contour matching is an important issue at the matching process. An efficient and robust shape-based image retrieval system is proposed. We use the Prompt edge detection method [H.J. Lin et al., (2001)] to detect edge points, which is compared with the Sobel edge detection method. We also introduce a shape representation method, the mountain-climbing sequence (MCS), that is invariant to translation, rotation, and scale problems. The results of our proposed method show a superior matching ratio even in the presence of a modest level of deformation.