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• ### 01 Introduction

This is a preview. There are 3 more flashcards available for chapter 01

• #### 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

• ### 1 Image Filtering

This is a preview. There are 4 more flashcards available for chapter 1

• #### What is an edge (derivative)?

An edge is a place of rapid change in the image intensity function
• #### 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 pixel

But using the separable filters, we reduce this to 2k operations per pixels (3 from top to bottom and 3 from left to right)
• #### What is the difference between cross-correlation and convolution?

Flip the filter (a, b, c...) > (i, h, g)
• #### Why do you use the Gaussian kernel?

To find edges.

The image is made smooth so the edges are better to be seen.
• #### 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)

• #### What is f(x, y)?

It gives the intensity at position (x, y)
• #### What is a color image?

Three functions pasted together

f(x,y) = [r(x,y) g(x,y) b(x,y)
• #### What is (vector) quantization?

-   The process of clustering features
-   Building the visual vocabulary

• #### Name three different types of noise

Salt and pepper noise (white and black pixels)

impulse noise (white pixels)

Gaussian noise (variations in intensity drawn from a Gaussian normal distribution