Category Independent Object Proposals


This work aims to provide a small pool of regions for an image, that are likely to contain every object in the image, regardless of category.


01/13/2014 - An expanded version of this work will appear in the February 2014 volume of PAMI. Please cite this version [pdf]
11/23/2010 - Precomputed proposals for Pascal VOC 2010 + Support for 64-bit Windows
9/21/2010 - Included support for 32-bit linux
9/20/2010 - The website is now up!
The code to generate proposals for an image is now available.


Category Independent Object Proposal Code PROP_code.tar.gz (15MB)
The code to generate a ranked pool of candidate object regions for each image.
This code is written in Matlab, with supporing compiled mex functions.
I've tested it on 32 and 64 bit linux machines (Matlab 2009b i.e. 7.9)
Annotated Dataset PROP_BSDS_ann.tar.gz (390KB)
These are the annotations we added to the Berkeley Segmentation Dataset.
These annotations provide the segmentations for each object, and respect the region boundaries of the original dataset.
PASCAL VOC 2010 Precomputed Proposals pascal_2010_proposals.tar.gz (15.6GB!)
Precomputed regions for each image in PASCAL VOC 2010 (train+val+test). Please note that this is a very large file.


[2] “Category-Independent Object Proposals With Diverse Ranking” [pdf]
Ian Endres, and Derek Hoiem
PAMI February 2014.
[1] “Category Independent Object Proposals” [pdf][slides]
Ian Endres, and Derek Hoiem
ECCV 2010.

This research is supported by NSF IIS-0904209: Physically Grounded Object Recognition.

Please contact iendres2 -at- with any questions.

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