{ "data_id": "19", "name": "segment", "exact_name": "segment", "version": 1, "version_label": "1", "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", "format": "ARFF", "uploader": "Jan van Rijn", "uploader_id": 1, "visibility": "public", "creator": null, "contributor": null, "date": "2014-04-06 23:22:10", "update_comment": null, "last_update": "2014-04-06 23:22:10", "licence": "Public", "status": "active", "error_message": null, "url": "https:\/\/www.openml.org\/data\/download\/36\/dataset_36_segment.arff", "default_target_attribute": "class", "row_id_attribute": null, "ignore_attribute": null, "runs": 11, "suggest": { "input": [ "segment", "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. 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