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# ILLINOIS CS 543 - Segmentation and Grouping

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Segmentation and Grouping•Motivation: not all information is evidence•Obtain a compact representation •from an image/motion sequence/set of tokens•Should support application•Broad theory is absent at present•Grouping (or clustering)•collect together tokens that “belong together”•Fitting•associate a model with tokens•issues•which model?•which token goes to which element?•how many elements in the model?General ideas•tokens•whatever we need to group (pixels, points, surface elements, etc., etc.)•top down segmentation•tokens belong together because they lie on the same object•bottom up segmentation•tokens belong together because they are locally coherent• These two are not mutually exclusive•e.g. symmetries, etc.Basic ideas of grouping in humans•Figure-ground discrimination•grouping can be seen in terms of allocating some elements to a figure, some to ground•impoverished theory• Gestalt properties• elements in a collection of elements can have properties that result from relationships (Muller Lyer effect)• gestaltqualitat•A series of factors affect whether elements should be grouped together•Gestalt factorsTechnique: Background Subtraction•If we know what the background looks like, it is easy to identify “interesting bits”•Applications•Person in an office•Tracking cars on a road•surveillance•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 pixelsTechnique: Shot Boundary Detection•Find the shots in a sequence of video•shot boundaries usually 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• Applications:•representation for movies, or video sequences •find shot boundaries•obtain “most representative” frame•supports searchSegmentation 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•Point-Cluster distance•single-link clustering•complete-link clustering•group-average clustering•Dendrograms•yield a picture of output as clustering process continuesDendrograms can inform choice of clustersFrom Ohlander et al, 1978From Ohlander et al, 1978K-Means•Choose a fixed number of clusters•Choose cluster centers and point-cluster allocations to minimize error •can’t do this by search•there are too many possible allocations.• Algorithm•fix cluster centers; allocate points to closest cluster•fix allocation; compute best cluster centers•x could be any set of features for which we can compute a distance (careful about scaling)€ xj−µi2j∈elements of i'th cluster∑      i∈clusters∑K-means clustering using intensity alone and color aloneImageClusters on intensity Clusters on colorK-means using color alone, 11 segmentsImageClusters on colorK-means usingcolor alone,11 segments.K-means using colour andposition, 20

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