Presentation 2011-06-06
A Study on Norm Selection for CV algorithms
Yuichiro MUKAI, Hiroshi OIKE, Toshikazu WADA,
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Abstract(in English) This paper discusses the importance of the norm selections for Computer Vision(CV) algorithms. L_p norm is often used as a dissimilarity measure between two vectors. For example, L_0 norm represents number of non-zero elements of a vector (assuming 0^0 ≡ 0), L_0 norm of a difference vector is called Manhattan Distance, and L_2 Norm is Euclidean distance. Because of the different poperties among these norms, CV algorithm can produce different result depending on the employed norm. We clarify that the following two problems are mainly caused by improper norm selections: "why Bag of Features based similar image search often finds the same image as similar to varieties of query images?" and "why discontinuous pixel values are estimated by discrete optimization based denoising algorithm employing smoothing energy term consisting of absolute sum of the neighboring pixel values?". Also, this paper shows these two problems are solved or relaxed just by changing the norms.
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Keyword(in English) Lp norm / Bag-of-Features / noise reduction / discrete optimization
Paper # DE2011-13,PRMU2011-44
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Committee DE
Conference Date 2011/5/30(1days)
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Registration To Data Engineering (DE)
Language JPN
Title (in Japanese) (See Japanese page)
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Title (in English) A Study on Norm Selection for CV algorithms
Sub Title (in English)
Keyword(1) Lp norm
Keyword(2) Bag-of-Features
Keyword(3) noise reduction
Keyword(4) discrete optimization
1st Author's Name Yuichiro MUKAI
1st Author's Affiliation Faculty of System Engineering, Wakayama University()
2nd Author's Name Hiroshi OIKE
2nd Author's Affiliation Faculty of System Engineering, Wakayama University
3rd Author's Name Toshikazu WADA
3rd Author's Affiliation Faculty of System Engineering, Wakayama University
Date 2011-06-06
Paper # DE2011-13,PRMU2011-44
Volume (vol) vol.111
Number (no) 76
Page pp.pp.-
#Pages 6
Date of Issue