# # Segmentation names INFO from http://www.ics.uci.edu/~mlearn/MLSummary.html # # Prepared for S/R by # R.W. Oldford 2004 # # # # Image segmentation Database # Donated by Carla Brodley # Documentation status: Skimpy # Not previously used in the ml literature as of 8/1991 # Image data described by high-level numeric-valued attributes, 7 classes # Ftp Access : http://www.ics.uci.edu/~mlearn/MLSummary.html # # # 1. Title: Image Segmentation data # # 2. Source Information # -- Creators: Vision Group, University of Massachusetts # -- Donor: Vision Group (Carla Brodley, brodley@cs.umass.edu) # -- Date: November, 1990 # # 3. Past Usage: None yet published # # 4. Relevant Information: # # The instances were drawn randomly from a database of 7 outdoor # images. The images were handsegmented to create a classification # for every pixel. # # Each instance is a 3x3 region. # # 5. Number of Instances: Training data: 210 Test data: 2100 # # 6. Number of Attributes: 19 continuous attributes # # 7. Attribute Information: # # 1. region-centroid-col: the column of the center pixel of the region. # 2. region-centroid-row: the row of the center pixel of the region. # 3. region-pixel-count: the number of pixels in a region = 9. # 4. short-line-density-5: the results of a line extractoin algorithm that # counts how many lines of length 5 (any orientation) with # low contrast, less than or equal to 5, go through the region. # 5. short-line-density-2: same as short-line-density-5 but counts lines # of high contrast, greater than 5. # 6. vedge-mean: measure the contrast of horizontally # adjacent pixels in the region. There are 6, the mean and # standard deviation are given. This attribute is used as # a vertical edge detector. # 7. vegde-sd: (see 6) # 8. hedge-mean: measures the contrast of vertically adjacent # pixels. Used for horizontal line detection. # 9. hedge-sd: (see 8). # 10. intensity-mean: the average over the region of (R + G + B)/3 # 11. rawred-mean: the average over the region of the R value. # 12. rawblue-mean: the average over the region of the B value. # 13. rawgreen-mean: the average over the region of the G value. # 14. exred-mean: measure the excess red: (2R - (G + B)) # 15. exblue-mean: measure the excess blue: (2B - (G + R)) # 16. exgreen-mean: measure the excess green: (2G - (R + B)) # 17. value-mean: 3-d nonlinear transformation # of RGB. (Algorithm can be found in Foley and VanDam, Fundamentals # of Interactive Computer Graphics) # 18. saturatoin-mean: (see 17) # 19. hue-mean: (see 17) # # 8. Missing Attribute Values: None # # 9. Class Distribution: # # Classes: brickface, sky, foliage, cement, window, path, grass. # # 30 instances per class for training data. # 300 instances per class for test data.