{"status":200,"statusText":"OK","data":{"uci_id":50,"name":"Image Segmentation","repository_url":"https://archive.ics.uci.edu/dataset/50/image+segmentation","data_url":"https://archive.ics.uci.edu/static/public/50/data.csv","abstract":"Image data described by high-level numeric-valued attributes, 7 classes","area":"Other","tasks":["Classification"],"characteristics":["Multivariate"],"num_instances":2310,"num_features":19,"feature_types":["Real"],"demographics":[],"target_col":["class"],"index_col":null,"has_missing_values":"no","missing_values_symbol":null,"year_of_dataset_creation":1990,"last_updated":"Fri Oct 27 2023","dataset_doi":"10.24432/C5GP4N","creators":[],"intro_paper":null,"variables":[{"name":"class","role":"Target","type":"Categorical","demographic":null,"description":null,"units":null,"missing_values":"no"},{"name":"region-centroid-col","role":"Feature","type":"Continuous","demographic":null,"description":"the column of the center pixel of the region","units":null,"missing_values":"no"},{"name":"region-centroid-row","role":"Feature","type":"Continuous","demographic":null,"description":"the row of the center pixel of the region","units":null,"missing_values":"no"},{"name":"region-pixel-count","role":"Feature","type":"Continuous","demographic":null,"description":"the number of pixels in a region = 9","units":null,"missing_values":"no"},{"name":"short-line-density-5","role":"Feature","type":"Continuous","demographic":null,"description":"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","units":null,"missing_values":"no"},{"name":"short-line-density-2","role":"Feature","type":"Continuous","demographic":null,"description":"same as short-line-density-5 but counts lines of high contrast, greater than 5","units":null,"missing_values":"no"},{"name":"vedge-mean","role":"Feature","type":"Continuous","demographic":null,"description":"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","units":null,"missing_values":"no"},{"name":"vedge-sd","role":"Feature","type":"Continuous","demographic":null,"description":"see 6","units":null,"missing_values":"no"},{"name":"hedge-mean","role":"Feature","type":"Continuous","demographic":null,"description":"measures the contrast of vertically adjacent pixels. Used for horizontal line detection.","units":null,"missing_values":"no"},{"name":"hedge-sd","role":"Feature","type":"Continuous","demographic":null,"description":"see 8","units":null,"missing_values":"no"},{"name":"intensity-mean","role":"Feature","type":"Continuous","demographic":null,"description":"the average over the region of (R + G + B)/3","units":null,"missing_values":"no"},{"name":"rawred-mean","role":"Feature","type":"Continuous","demographic":null,"description":"the average over the region of the R value.","units":null,"missing_values":"no"},{"name":"rawblue-mean","role":"Feature","type":"Continuous","demographic":null,"description":"the average over the region of the B value.","units":null,"missing_values":"no"},{"name":"rawgreen-mean","role":"Feature","type":"Continuous","demographic":null,"description":"the average over the region of the G value.","units":null,"missing_values":"no"},{"name":"exred-mean","role":"Feature","type":"Continuous","demographic":null,"description":"measure the excess red:  (2R - (G + B))","units":null,"missing_values":"no"},{"name":"exblue-mean","role":"Feature","type":"Continuous","demographic":null,"description":"measure the excess blue:  (2B - (G + R))","units":null,"missing_values":"no"},{"name":"exgreen-mean","role":"Feature","type":"Continuous","demographic":null,"description":"measure the excess green:  (2G - (R + B))","units":null,"missing_values":"no"},{"name":"value-mean","role":"Feature","type":"Continuous","demographic":null,"description":"3-d nonlinear transformation of RGB. (Algorithm can be found in Foley and VanDam, Fundamentals of Interactive Computer Graphics)","units":null,"missing_values":"no"},{"name":"saturation-mean","role":"Feature","type":"Continuous","demographic":null,"description":"see 17","units":null,"missing_values":"no"},{"name":"hue-mean","role":"Feature","type":"Continuous","demographic":null,"description":"see 17","units":null,"missing_values":"no"}],"additional_info":{"summary":"The instances were drawn randomly from a database of 7 outdoor images.  The images were handsegmented to create a classification for every pixel.  \r\n\r\n   Each instance is a 3x3 region.","purpose":null,"funded_by":null,"instances_represent":null,"recommended_data_splits":null,"sensitive_data":null,"preprocessing_description":null,"variable_info":"    1.  region-centroid-col:  the column of the center pixel of the region.\r\n    2.  region-centroid-row:  the row of the center pixel of the region.\r\n    3.  region-pixel-count:  the number of pixels in a region = 9.\r\n    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.\r\n    5.  short-line-density-2:  same as short-line-density-5 but counts lines of high contrast, greater than 5.\r\n    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.\r\n    7.  vegde-sd:  (see 6)\r\n    8.  hedge-mean:  measures the contrast of vertically adjacent pixels. Used for horizontal line detection. \r\n    9.  hedge-sd: (see 8).\r\n    10. intensity-mean:  the average over the region of (R + G + B)/3\r\n    11. rawred-mean: the average over the region of the R value.\r\n    12. rawblue-mean: the average over the region of the B value.\r\n    13. rawgreen-mean: the average over the region of the G value.\r\n    14. exred-mean: measure the excess red:  (2R - (G + B))\r\n    15. exblue-mean: measure the excess blue:  (2B - (G + R))\r\n    16. exgreen-mean: measure the excess green:  (2G - (R + B))\r\n    17. value-mean:  3-d nonlinear transformation of RGB. (Algorithm can be found in Foley and VanDam, Fundamentals of Interactive Computer Graphics)\r\n    18. saturatoin-mean:  (see 17)\r\n    19. hue-mean:  (see 17)","citation":null}}}