DocumentCode :
3357142
Title :
Similarity Measurement Based on Trigonometric Function Distance
Author :
Li, Zongmin ; Kunpeng Hou ; Li, Hua
Author_Institution :
Sch. Of Comput. Sci. & Commun. Eng., Pet. Univ., Shandong
fYear :
2006
fDate :
3-5 Aug. 2006
Firstpage :
227
Lastpage :
231
Abstract :
With the research and analysis on similarity measures which are commonly used in cross-media retrieval and content based image retrieval (CBIR), a new method called trigonometric function distance is proposed. This method satisfies metric properties, and is better than Euclidean distance and Minkowski distance in image similarity. To support this new theory, an algorithm for object shape analysis is designed, and experiments based on trigonometric function distance are conducted. Experiments give an encouraging high recognition rate by using the new similarity measurement
Keywords :
content-based retrieval; image recognition; image retrieval; Euclidean distance; Minkowski distance; content based image retrieval; cross-media retrieval; image recognition; image similarity measurement; trigonometric function distance; Algorithm design and analysis; Application software; Computer science; Content based retrieval; Covariance matrix; Euclidean distance; Image matching; Image retrieval; Pervasive computing; Shape measurement; image retrieval; similarity measurement; trigonometric function distance;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Pervasive Computing and Applications, 2006 1st International Symposium on
Conference_Location :
Urumqi
Print_ISBN :
1-4244-0326-x
Electronic_ISBN :
1-4244-0326-x
Type :
conf
DOI :
10.1109/SPCA.2006.297573
Filename :
4079144
Link To Document :
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