HooverCompareSegmentation - Hoover compare segmentation

Compare two segmentations with Hoover metrics

Detailed description

This application compares a machine segmentation (MS) with a partial ground truth segmentation (GT). The Hoover metrics are used to estimate scores for correct detection, over-segmentation, under-segmentation and missed detection.
The application can output the overall Hoover scores along with coloredimages of the MS and GT segmentation showing the state of each region (correct detection, over-segmentation, under-segmentation, missed) The Hoover metrics are described in : Hoover et al., “An experimental comparison of range image segmentation algorithms”, IEEE PAMI vol. 18, no. 7, July 1996.


This section describes in details the parameters available for this application. Table [1] presents a summary of these parameters and the parameters keys to be used in command-line and programming languages. Application key is HooverCompareSegmentation .

[1]Table: Parameters table for Hoover compare segmentation.
Parameter Key Parameter Name Parameter Type
ingt Input ground truth Input image
inms Input machine segmentation Input image
bg Background label Int
th Overlapping threshold Float
outgt Colored ground truth output Output image
outms Colored machine segmentation output Output image
rc Correct detection score Float
rf Over-segmentation score Float
ra Under-segmentation score Float
rm Missed detection score Float
inxml Load otb application from xml file XML input parameters file
outxml Save otb application to xml file XML output parameters file
  • Input ground truth: A partial ground truth segmentation image.
  • Input machine segmentation: A machine segmentation image.
  • Background label: Label value of the background in the input segmentations.
  • Overlapping threshold: Overlapping threshold used to find Hoover instances.
  • Colored ground truth output: The colored ground truth output image.
  • Colored machine segmentation output: The colored machine segmentation output image.
  • Correct detection score: Overall score for correct detection (RC).
  • Over-segmentation score: Overall score for over segmentation (RF).
  • Under-segmentation score: Overall score for under segmentation (RA).
  • Missed detection score: Overall score for missed detection (RM).
  • Load otb application from xml file: Load otb application from xml file.
  • Save otb application to xml file: Save otb application to xml file.


To run this example in command-line, use the following:

otbcli_HooverCompareSegmentation -ingt maur_GT.tif -inms maur_labelled.tif -outgt maur_colored_GT.tif uint8

To run this example from Python, use the following code snippet:


# Import the otb applications package
import otbApplication

# The following line creates an instance of the HooverCompareSegmentation application
HooverCompareSegmentation = otbApplication.Registry.CreateApplication("HooverCompareSegmentation")

# The following lines set all the application parameters:
HooverCompareSegmentation.SetParameterString("ingt", "maur_GT.tif")

HooverCompareSegmentation.SetParameterString("inms", "maur_labelled.tif")

HooverCompareSegmentation.SetParameterString("outgt", "maur_colored_GT.tif")
HooverCompareSegmentation.SetParameterOutputImagePixelType("outgt", 1)

# The following line execute the application




This application has been written by OTB-Team.

See Also

These additional resources can be useful for further information:

otbHooverMatrixFilter, otbHooverInstanceFilter, otbLabelMapToAttributeImageFilter