Human-Computer Interaction Human-Computer Interaction Segmentation Hanyang University Jong-Il Park

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

Segmentation codes