# Datasets

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Evaluation was based on five datasets; these are briefly introduced in the following. 
The pre-processing, formatting and conversion tools used are detailed afterwards.

Dataset             | Link
--------------------|-----
[BSDS500](bsds500)  | [Web](https://www2.eecs.berkeley.edu/Research/Projects/CS/vision/grouping/resources.html)
[NYUV2](nyuv2)      | [Web](http://cs.nyu.edu/~silberman/datasets/nyu_depth_v2.html)
[SBD](sbd)          | [Web](http://dags.stanford.edu/projects/scenedataset.html)
[SUNRGBD](sunrgbd)  | [Web](http://rgbd.cs.princeton.edu/)
[Fash](fash)        | [Web](http://vision.is.tohoku.ac.jp/~kyamagu/research/clothing_parsing/)

**Downloads of the pre-processed datasets will be made available here: 
[davidstutz/superpixel-benchmark-data](https://github.com/davidstutz/superpixel-benchmark-data)**

Sample images of all datasets are shown below:

![Dataset overview.](DATASETS.png?raw=true "Dataset overview.")

## BSDS500

The Berkeley Segmentation Dataset 500 [2] was the first dataset used for evaluating
superpixel algorithms. It consists of 500 images, each with 5 different ground truth
segmentations of high quality, divided into a training set of 200 images, a validation
set of 100 images and a test set of 200 images. For parameter optimization, the validation
set was used.

    [2] P. Arbelaez, M. Maire, C. Fowlkes, J. Malik.
        Contour detection and hierarchical image segmentation.
        IEEE Transactions on Pattern Analysis and Machine Intelligence 33 (5) (2011) 898-916.

The ground truth was used as provided, however, converted from `.mat` format 
to `.csv` format. The converted dataset is not yet available.

In order to manually convert the BSDS500 dataset, use `lib_tools/bsds500_convert_script.m`:

1. Download the BSDS500 dataset from [here](https://www2.eecs.berkeley.edu/Research/Projects/CS/vision/grouping/resources.html).
2. Extract the `BSR/BSDS500/data` folder into `data/BSDS500` (overwriting the provided 
examples in `data/BSDS500/images` and `data/BSDS500/csv_groundTruth`). Also note 
that afterwards there are three folders: `groundTruth`, `csv_groundTruth` and `images`.
3. In `lib_tools/bsds500_convert_script.m`, adapt the path to the directory, 
i.e. set `BSDS500_DIR` correctly.
4. Run the script. Note that this may take some time.

The instructions are also found in `lib_tools/bsds500_convert_script.m`.

## NYUV2

The NYU Depth Dataset V2 [3] includes 1449 images with pre-processed depth.
Semantic ground truth segmentations with instance labels are provided.
Following Ren and Bo [4], the ground truth has been pre-processed to
remove small unlabeled segments. 199 images were randomly chosen to represent the
validation set and 399 images where randomly chosen for testing.

    [3] N. Silberman, D. Hoiem, P. Kohli, R. Fergus.
        Indoor segmentation and support inference from RGBD images.
        European Conference on Computer Vision, 2012, pp. 746–760.
    [4] X. Ren, L. Bo.
        Discriminatively trained sparse code gradients for contour detection.
        Neural Information Processing Systems, 2012, pp. 593–601.

The randomly chosen validation and test subsets can be found in `data/NYUV2/nyuv2_train_subset.txt`
and `data/NYUV2/nyuv2_test_subset.txt` (note that the validation set corresponds to
the train set in this case).

The ground truth was converted to `.csv` files after thinning unlabeled regions.
The converted dataset is available in the data repository:
[davidstutz/superpixel-benchmark-data](https://github.com/davidstutz/superpixel-benchmark-data).

In order to manually convert the NYUV2 dataset and extract the used validation and
testing subsets, use `lib_tools/nyuv2_convert.script.m`:

1. Download the dataset from [here](http://cs.nyu.edu/~silberman/datasets/nyu_depth_v2.html).
Make sure that the downloaded file is `nyu_depth_v2_labeled.mat`.
2. Put the file in `data/NYUV2/`.
3. Make sure that `data/NYUV2` contains `nyuv2_test_subset.txt`, 
`nyuv2_train_subset.txt`, `nyuv2_test.txt` and `nyuv2_train.txt`.
4. In `lib_tools/nyuv2_convert.script.m`, set `NYUV2_DIR` to point to the `data/NYUV2` directory.
5. Run the script. Note that this may take some time and memory.

The instructions are also found in `lib_tools/nyuv2_convert.script.m`.

## SBD

The Stanford Background Dataset [5] combines 715 images from several datasets. 
The images are of varying size, quality and scenes. The semantic ground truth
segmentations provided needed to be pre-processed in order to guarantee connected components.
Validation and testing sets of size 238 and 477, respectively, were chosen at random.

    [5] S. Gould, R. Fulton, D. Koller.
        Decomposing a scene into geometric and semantically consistent regions.
        International Conference on Computer Vision, 2009, pp. 1–8.

The ground truth was converted to `.csv` files. The converted dataset 
is available in the data repository: 
[davidstutz/superpixel-benchmark-data](https://github.com/davidstutz/superpixel-benchmark-data).

To manually convert the SBD and select validation and testing images, follow
`lib_tools/sbd_convert_script.m`:

1. Download the Stanford Background Dataset from [here](http://dags.stanford.edu/projects/scenedataset.html).
2. Extract the dataset such that `data/SBD` contains two folders: `images` and `labels`.
3. Make sure that `data/SBD` contains `sbd_test.txt` and `sbd_train.txt`.
4. In `lib_tools/sbd_convert_script.m`, adapt the variable `SBD_DIR` below to match the path to `data/SBD`.
5. Run the script. Note that this may take some time and memory.

The instructions are also found in `lib_tools/sbd_convert_script.m`.

## SUNRGBD

The SUNRGBD dataset [6] contains 10335 images including pre-processed depth. 
Semantic ground truth segmentations are provided and need to be pre-processed
similar to the NYUV2 dataset. Validation set and testing set of size 200 and 400, 
respectively, were chosen at random. Images that are also included in the NYUV2 
dataset were ignored.

    [6] S. Song, S. P. Lichtenberg, J. Xiao.
        SUN RGB-D: A RGB-D scene understanding benchmark suite.
        IEEE Conference on Computer Vision and Pattern Recognition, 2015, pp. 567–576.

The ground truth was converted to `.csv` files. The converted dataset
is available in the data repository:
[davidstutz/superpixel-benchmark-data](https://github.com/davidstutz/superpixel-benchmark-data).

To manually convert the dataset, follow `lib_tools/sunrgbd_convert_script.m`:

1. Download the SUNRGBD dataset from [here](http://rgbd.cs.princeton.edu/).
2. Make sure to download both the SUNRGBD V1 dataset and the SUNRGBDtoolbox containing the annotations.
3. From the SUNRGBDtoolbox extract `SUNRGBD2dseg.mat` and `SUNRGBDMeta.mat` to `data/SUNRGBD`.
4. From the SUNRGBD V1 dataset extract all files into `data/SUNRGBD`; note that this may take quite some time!
It might be wise to extract the contained directories (xtion, realsense, kv1, kv2) separately.
5. In `lib_tools/sunrgbd_convert_script.m`, adapt `ROOT_DIR` to point to the data directory
(i.e. the parent directory of the SUNRGBD directory).
5. Run the script. Note that this may take some time and memory.

The instructions are also found in `lib_tools/sunrgbd_convert_script.m`.

## Fash

The Fashionista dataset [7] contains 685 images with semantic ground truth segmentations.
The ground truth segmentations were pre-processed to ensure connected segments.
Validation set and training set of size 222 and 463, respectively, were chosen at random.

    [7] K. Yamaguchi, K. M. H, L. E. Ortiz, T. L. Berg.
        Parsing clothing in fashion photographs.
        IEEE Conference on Computer Vision and Pattern Recognition, 2012, pp. 3570–3577.

The ground truth needs to be converted to `.csv` files using the steps in `lib_tools/fash_convert_script.m`:

1. Download the Fashionista dataset from [here](http://vision.is.tohoku.ac.jp/~kyamagu/research/clothing_parsing/).
2. Extract fashionista_v0.2.1.mat into data/Fash.
3. Adapt the below variables to match the path where `data/Fash` can be found.
4. Run the script. Note that this may take some time and memory.