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A snapshot of the summary - Projections
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01 Introduction
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Why visual search engine?- Growing data requires more efficient solution- Manual indexing is costly and time-consuming- New technologies are needed to automate processes and to unlock possibilities of big data- Metadata standards are needed
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1 Image Filtering
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What is an edge (derivative)?An edge is a place of rapid change in the image intensity function
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What is the computational complexity advantage for a separable filter of size k x k, in terms of number of operations per output pixel?For a k x k Gaussian filter, 2D convolution requires k^2 operations per pixelBut using the separable filters, we reduce this to 2k operations per pixels (3 from top to bottom and 3 from left to right)
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What is the difference between cross-correlation and convolution?Flip the filter (a, b, c...) > (i, h, g)
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Why do you use the Gaussian kernel?To find edges.The image is made smooth so the edges are better to be seen.
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How to filter noise?1. Let's replace each pixel with an average of all the values in its neighbourhood.2. Apply a gaussian filter.Correlation filtering ( G = H (X) F)Convolution (G = H * F)
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What is f(x, y)?It gives the intensity at position (x, y)
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What is a color image?Three functions pasted togetherf(x,y) = [r(x,y) g(x,y) b(x,y)
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What is (vector) quantization?- The process of clustering features- Building the visual vocabulary
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Name three different types of noiseSalt and pepper noise (white and black pixels)impulse noise (white pixels)Gaussian noise (variations in intensity drawn from a Gaussian normal distribution
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