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fa13-cs188-lecture-22-1PP

# 0 dot product of two images vectors 1 usually

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Unformatted text preview: est neighbor for digits:   Take new image   Compare to all training images   Assign based on closest example 0 1   Encoding: image is vector of intensi)es: 2   What’s the similarity func)on? 0   Dot product of two images vectors? 1   Usually normalize vectors so ||x|| = 1   min = 0 (when?), max = 1 (when?) 2 Similarity Func)ons Basic Similarity   Many similari)es based on feature dot products:   If features are just the pixels:   Note: not all similari)es are of this form Invariant Metrics   BeOer similarity func)ons use knowledge about vision   Example: invariant metrics:   Similari)es are invariant under certain transforma)ons   Rota)on, scaling, transla)on, stroke ­thickness…   E.g:   16 x 16 = 256 pixels; a point in 256 ­dim space   These points have small similarity in R256 (why?)   How can we incorporate such invariances into our similari)es? This and next few slides adapted from Xiao Hu, UIUC Rota)on Invariant Metrics   Each example is now a curve in R256   Rota)on invariant similarity: s’=max s( r( ), r( ))   E.g. highest similarity between images’ rota)on lines Template Deforma)on   Deformable templates:         An “ideal” version of ea...
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