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    "description": "**Author**:   \n**Source**: Unknown -   \n**Please cite**:   \n\n1. Title: Image Segmentation data\n \n 2. Source Information\n    -- Creators: Vision Group, University of Massachusetts\n    -- Donor: Vision Group (Carla Brodley, brodley@cs.umass.edu)\n    -- Date: November, 1990\n  \n 3. Past Usage: None yet published\n \n 4. Relevant Information:\n \n    The instances were drawn randomly from a database of 7 outdoor \n    images.  The images were handsegmented to create a classification\n    for every pixel.  \n \n    Each instance is a 3x3 region.\n \n 5. Number of Instances: Training data: 210  Test data: 2100\n \n 6. Number of Attributes: 19 continuous attributes\n \n 7. Attribute Information:\n \n     1.  region-centroid-col:  the column of the center pixel of the region.\n     2.  region-centroid-row:  the row of the center pixel of the region.\n     3.  region-pixel-count:  the number of pixels in a region = 9.\n     4.  short-line-density-5:  the results of a line extractoin algorithm that \n          counts how many lines of length 5 (any orientation) with\n          low contrast, less than or equal to 5, go through the region.\n     5.  short-line-density-2:  same as short-line-density-5 but counts lines\n          of high contrast, greater than 5.\n     6.  vedge-mean:  measure the contrast of horizontally\n          adjacent pixels in the region.  There are 6, the mean and \n          standard deviation are given.  This attribute is used as\n         a vertical edge detector.\n     7.  vegde-sd:  (see 6)\n     8.  hedge-mean:  measures the contrast of vertically adjacent\n           pixels. Used for horizontal line detection. \n     9.  hedge-sd: (see 8).\n     10. intensity-mean:  the average over the region of (R + G + B)\/3\n     11. rawred-mean: the average over the region of the R value.\n     12. rawblue-mean: the average over the region of the B value.\n     13. rawgreen-mean: the average over the region of the G value.\n     14. exred-mean: measure the excess red:  (2R - (G + B))\n     15. exblue-mean: measure the excess blue:  (2B - (G + R))\n     16. exgreen-mean: measure the excess green:  (2G - (R + B))\n     17. value-mean:  3-d nonlinear transformation\n          of RGB. (Algorithm can be found in Foley and VanDam, Fundamentals\n          of Interactive Computer Graphics)\n     18. saturatoin-mean:  (see 17)\n     19. hue-mean:  (see 17)\n \n 8. Missing Attribute Values: None\n \n 9. Class Distribution: \n \n    Classes:  brickface, sky, foliage, cement, window, path, grass.\n \n    30 instances per class for training data.\n    300 instances per class for test data.\n \n\n\n\n\n Relabeled values in attribute class\n    From: 1                       To: brickface           \n    From: 2                       To: sky                 \n    From: 3                       To: foliage             \n    From: 4                       To: cement              \n    From: 5                       To: window              \n    From: 6                       To: path                \n    From: 7                       To: grass",
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    "uploader": "Jan van Rijn",
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    "date": "2014-04-06 23:22:10",
    "update_comment": null,
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            "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  "
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