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Title:
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MULTI-BOUNDARY SHAPE RETRIEVAL BASED ON A NEW CLASS OF MOMENT FUNCTIONS |
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Author(s):
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Ruixia Song, Xiaochun Wang, Yena Wang and Mei Gu |
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ISBN:
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978-989-8533-38-8 |
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Editors:
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Katherine Blashki and Yingcai Xiao |
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Year:
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2015 |
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Edition:
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Single |
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Keywords:
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Content based image retrieval; Multi-boundary shape retrieval; Orthogonal moment functions; V-system; V-moment. |
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Type:
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Full Paper |
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First Page:
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179 |
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Last Page:
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186 |
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Language:
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English |
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Cover:
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Full Contents:
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click to dowload 
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Paper Abstract:
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Based on a class of complete orthogonal function system, V-system, this paper proposes a new kind of moment functions (called V-moment functions), and applies them to the shape retrieval. The V-moments are orthogonal, and involve only simple linear operations. The V-moments can be used to extract image features accurately, and the original image can be reconstructed with only a small amount of them. The V-moments have advantage in extracting features of image with complex boundaries since the V-system contains a great deal of discontinuous basis functions. Therefore, feature extraction of multi-boundary image using V-moments is very promising. This paper performs image retrieval based on their shape features. The results of retrieval experiment, conducted on benchmark database MPEG-7-shape-CE2, show that the algorithm proposed in this paper outperforms some classical moments including Zernike moments, Hu invariant moments, orthogonal Fourier-Mellin moments, Legendre moments and the geometric central moments in retrieval efficiency according to several evaluation indexes |
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