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Perceptually-based Comparison of Image Similarity Metrics

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dc.creator Russell, Richard
dc.creator Sinha, Pawan
dc.date 2004-10-20T21:03:39Z
dc.date 2004-10-20T21:03:39Z
dc.date 2001-07-01
dc.date.accessioned 2013-10-09T02:48:37Z
dc.date.available 2013-10-09T02:48:37Z
dc.date.issued 2013-10-09
dc.identifier AIM-2001-014
dc.identifier CBCL-201
dc.identifier http://hdl.handle.net/1721.1/7235
dc.identifier.uri http://koha.mediu.edu.my:8181/xmlui/handle/1721
dc.description The image comparison operation ??sessing how well one image matches another ??rms a critical component of many image analysis systems and models of human visual processing. Two norms used commonly for this purpose are L1 and L2, which are specific instances of the Minkowski metric. However, there is often not a principled reason for selecting one norm over the other. One way to address this problem is by examining whether one metric better captures the perceptual notion of image similarity than the other. With this goal, we examined perceptual preferences for images retrieved on the basis of the L1 versus the L2 norm. These images were either small fragments without recognizable content, or larger patterns with recognizable content created via vector quantization. In both conditions the subjects showed a consistent preference for images matched using the L1 metric. These results suggest that, in the domain of natural images of the kind we have used, the L1 metric may better capture human notions of image similarity.
dc.format 13 p.
dc.format 9714300 bytes
dc.format 2612761 bytes
dc.format application/postscript
dc.format application/pdf
dc.language en_US
dc.relation AIM-2001-014
dc.relation CBCL-201
dc.subject AI
dc.subject Image matching
dc.subject vector quantization
dc.subject Minkowski metric
dc.title Perceptually-based Comparison of Image Similarity Metrics


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