Presentation 2010-03-16
A Study on Degradation Tolerant Dissimilarity Measures
Aiko Oka, Toshikazu Wada,
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Abstract(in English) This report presents a set of dissimilarity measures suitable for image retrieval and matching tasks with degraded query images. Since local image features such as SIFT can easily be affected by global image degradations like motion blurring, we have to use pixel-by-pixel image dissimilarity measure. In this report, we confine the degradations to lacking of terms in arbitrary orthogonal expansion. In this case, if remained expansion coefficients match between two images, they should be the right pair. We first propose a method for computing sparse difference vectors consisting of coefficient differences for the right pairs. Next, a set of metric defined on vectors that take smaller values for sparser vectors while keeping the same L2 norm. Our dissimilarity measures are the combination of the difference vector computation and the sparsity sensitive metrics. Through the experiments, we confirmed that the one-by-one SQI matching rate by our measure is over two times bigger than that of normalized correlation for Extended Yale B Face database (subset 4).
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Keyword(in English) Dissimirality measures / Image retrieval / Image identification
Paper # PRMU2009-306,HIP2009-191
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Committee HIP
Conference Date 2010/3/8(1days)
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Language JPN
Title (in Japanese) (See Japanese page)
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Title (in English) A Study on Degradation Tolerant Dissimilarity Measures
Sub Title (in English)
Keyword(1) Dissimirality measures
Keyword(2) Image retrieval
Keyword(3) Image identification
1st Author's Name Aiko Oka
1st Author's Affiliation Faculty of Systems Engineering, Wakayama University()
2nd Author's Name Toshikazu Wada
2nd Author's Affiliation Faculty of Systems Engineering, Wakayama University
Date 2010-03-16
Paper # PRMU2009-306,HIP2009-191
Volume (vol) vol.109
Number (no) 471
Page pp.pp.-
#Pages 6
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