Learning a nonlinear distance metric for supervised region-merging image segmentation
Computer Vision and Image Understanding
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THIS PAPER DESCRIBES A WORKING SYSTEM FOR SEGMENTING IMAGES OF COMPLEX SCENES. THE SYSTEM HAS EVOLVED OUT OF MANY YEARS OF RESEARCH IN LOW-LEVEL IMAGE SEGMENTATION AT THE UNIVERSITY OF MASSACHUSETTS. SEGMENTATIONS PRODUCED BY THE SYSTEM ARE USED EXTENSIVELY IN THE IMAGE INTERPRETATION WORK HERE. THE SYSTEM FIRST PRODUCES SEGMENTATIONS BASED UPON AN ANALYSIS OF SPATIALLY LOCALIZED FEATURE HISTOGRAMS. THESE SEGMENTATIONS ARE THEN POST-PROCESSED WITH A REGION MERGING ALGORITHM. A SIMPLE EXTENSION OF THE LOCAL HISTOGRAM ALGORITHM TO MULTIPLE FEATURES IS PRESENTED. ISSUES OF PARAMETER SELECTION FOR THE LOCAL HISTOGRAM SEGMENTATION ALGORITHM ARE ADDRESSED BY PRESENTING A MAPPING FROM THE MULTI-DIMENSIONAL PARAMETER SPACE TO A ONE DIMENSIONAL `SENSITIVITY'' SPACE. RESULTS ARE INCLUDED WHICH DEMONSTRATE THE ROBUST CHARACTER OF THE ALGORITHMS WHEN APPLIED TO OUTDOOR AND AERIAL IMAGERY.