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## PowerPoint Slideshow about ' Human-Computer Interaction Segmentation' - edana

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Why segment images?

- Form large chunks of pixels that can be dealt with together
- for efficiency
- because these might represent objects
- Join up image tokens that together convey information

Grouping

- Humans interpret image information collectively
- in “groups”
- Eg. Muller-Lyer illusion

Applications

- Shot boundary detection
- summarize video by
- find shot boundaries
- obtain “most representative” frame
- Background subtraction
- find “interesting bits” of image by subtracting known background
- Eg. find person in an office
- Eg. find cars on a road
- Interactive segmentation
- user marks some foreground/background pixels
- system cuts object out of image
- useful for image editing, etc.

Technique: Shot Boundary Detection

- Find the shots in a sequence of video
- shot boundaries cause big differences between succeeding frames
- Strategy:
- compute interframe distances
- declare a boundary where these are big
- Possible distances
- frame differences
- histogram differences
- block comparisons
- edge differences

Technique: Background Subtraction

- If we know the background, easy to find “interesting bits”
- Approach:
- use a moving average to estimate background image
- subtract from current frame
- large absolute values are interesting pixels
- trick: use morphological operations to clean up pixels

Interactive segmentation

- Goals
- User cuts an object out of one image to paste into another
- User forms a matte
- weights between 0 and 1 to mix pixels with background
- to cope with, say, hair
- Interactions
- mark some foreground, background pixels with strokes
- put a box around foreground
- Technical problem
- allocate pixels to foreground/background class
- consistent with interaction information
- segments are internally coherent and different from one another

Superpixels

- Pixels are too small and too detailed a representation
- for recognition
- for some kinds of reconstruction
- Replace with superpixels
- small groups of pixels that are
- clumpy
- like one another
- a reasonable representation of the underlying pixels

Segmentation as clustering

- Cluster together (pixels, tokens, etc.) that belong together
- Agglomerative clustering
- attach closest to cluster it is closest to
- repeat
- Divisive clustering
- split cluster along best boundary
- repeat

The watershed algorithm

- An agglomerative clusterer with a special metric

Clustering pixels

- Natural to use k-means
- represent pixels with
- intensity vector; color vector; vector of nearby filter responses
- perhaps position

The Mean Shift Algorithm

- Originally intended to find modes in scattered data
- Strategy
- start at a promising estimate of mode
- iterate until the estimate doesn’t change
- fit a model of probability density to some points near estimate
- find the peak of this model
- Model
- smoothing kernel
- the update takes a special form
- shift the mode to a weighted mean of the nearby points
- hence the name.

Clustering with Mean Shift

- Model data points as samples from a probability model
- clusters are associated with modes
- but it might be hard to find one mode per cluster
- if there’s more than one mode per cluster, they should be close together
- Apply mean shift to find modes
- modes should form small, widely separated clusters
- Now cluster the modes with (say) agglomerative clusterer
- easy, because there are small, widely separated clusters
- Point belongs to cluster that its closest mode belongs to

Mean Shift Segmentation

- Cluster pixels using mean shift
- each cluster is a segment
- Represent with color, position
- important
- color distances are not the same as position distances
- choose one scale for each

Evaluating Segmenters

- Collect “correct” segmentations
- from human labellers
- these may not be perfect, but ...
- Now apply your segmenter
- Count
- % human boundary pixels close to your boundary pixels -- Recall
- % of your boundary pixels close to human boundary pixels -- Precision

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