DOI: 10.5281/zenodo.257722
title: 2013 Imageclef Webupv Collection

description: This document describes the WEBUPV dataset compiled for the ImageCLEF<br>
2013 Scalable Concept Image Annotation task. The data mentioned here<br>
indicates what is ready for download. However, upon request or<br>
depending on feedback from the participants, additional data may be<br>
released.

The following is the directory structure of the collection, and bellow<br>
there is a brief description of what each compressed file<br>
contains.

Any publication in which this data has been used is required to cite the<br>
following paper:

@inproceedings{Villegas13_CLEF,<br>
  author = {Mauricio Villegas and Roberto Paredes and Bart Thomee},<br>
  title = {{O}verview of the {ImageCLEF} 2013 {S}calable {C}oncept {I}mage {A}nnotation {S}ubtask},<br>
  booktitle = {CLEF 2013 Evaluation Labs and Workshop, Online Working Notes},<br>
  year = {2013},<br>
  month = {September 23-26},<br>
  address = {Valencia, Spain},<br>
  isbn = {978-88-904810-5-5},<br>
  issn = {2038-4963},<br>
}

If the 'hsvcolorhist' and/or the 'lbpcenter' visual features are used, then it<br>
is also required to cite:

@inproceedings{SanchezOro13_CLEF,<br>
  author = {Jes\'us S\'anchez-Oro and Soto Montalvo and Antonio S. Montemayor and Juan J. Pant<br>
rigo and Abraham Duarte and V\'ictor Fresno and Raquel Mart\'inez},<br>
  title = {{URJC\&UNED} at {ImageCLEF} 2013 {P}hoto {A}nnotation {T}ask},<br>
  booktitle = {CLEF 2013 Evaluation Labs and Workshop, Online Working Notes},<br>
  year = {2013},<br>
  month = {September 23-26},<br>
  address = {Valencia, Spain},<br>
  isbn = {978-88-904810-5-5},<br>
  issn = {2038-4963},<br>
}

Directory structure<br>
-------------------

.<br>
|<br>
|--- README.txt<br>
|--- md5sums.txt<br>
|--- webupv13_train_lists.zip<br>
|--- webupv13_devel_lists.zip<br>
|--- webupv13_test_lists.zip<br>
|--- webupv13_baseline.zip<br>
|<br>
|--- feats_textual/<br>
|      |<br>
|      |--- webupv13_train_textual_pages.zip<br>
|      |--- webupv13_train_textual.scofeat.gz<br>
|      |--- webupv13_train_textual.keywords.gz<br>
|<br>
|--- feats_visual/<br>
       |<br>
       |--- webupv13_{train|devel|test}_visual_images.zip<br>
       |--- webupv13_{train|devel|test}_visual_gist.feat.gz<br>
       |--- webupv13_{train|devel|test}_visual_sift_1000.feat.gz<br>
       |--- webupv13_{train|devel|test}_visual_csift_1000.feat.gz<br>
       |--- webupv13_{train|devel|test}_visual_rgbsift_1000.feat.gz<br>
       |--- webupv13_{train|devel|test}_visual_opponentsift_1000.feat.gz<br>
       |--- webupv13_{train|devel|test}_visual_colorhist.feat.gz<br>
       |--- webupv13_{train|devel|test}_visual_getlf.feat.gz<br>
       |--- webupv13_{train|devel|test}_visual_hsvcolorhist.feat.gz<br>
       |--- webupv13_{train|devel|test}_visual_lbpcenter.feat.gz

<br>
Contents of files<br>
-----------------

* webupv13_train_lists.zip

  -&gt; train_iids.txt : IDs of the images (IIDs) in the training set<br>
                      (250000).

  -&gt; train_rids.txt : IDs of the webpages (RIDs) in the training set<br>
                      (262526).

  -&gt; train_*urls.txt : The original URLs from where the images (iurls)<br>
       and the webpages (rurls) were downloaded. Each line in the file<br>
       corresponds to an image, starting with the IID and is followed<br>
       by one or more URLs.

  -&gt; train_rimgsrc.txt : The URLs of the images as referenced in each<br>
       of the webpages. Each line of the file is of the form: IID RID<br>
       URL1 [URL2 ...]. This information is necessary to locate the<br>
       images within the webpages and it can also be useful as a<br>
       textual feature.

 

* webupv13_devel_lists.zip

  -&gt; devel_iids.txt : IDs of the images in the development set (1000).

  -&gt; devel_*urls.txt : The original URLs from where the images (iurls)<br>
       and the webpages (rurls) were downloaded. Each line in the file<br>
       corresponds to an image, starting with the IID and is followed<br>
       by one or more URLs.

       Note: These are included only to acknowledge the source of the<br>
       data, not be used as input to the annotation systems.

  -&gt; devel_concepts.txt : List concepts for the development set.

  -&gt; devel_gnd.txt : Ground truth concepts for the development set<br>
                     images.

  The concepts are defined by one or more WordNet synsets, which is<br>
  intended to make it possible to easily obtain more information about<br>
  the concepts, e.g. synonyms. In the concept list, the first column<br>
  (which is the name of the concept) indicates the word to search in<br>
  WordNet, the second column the synset type (either noun or<br>
  adjective), the third column is the sense number and the fourth<br>
  column is the WordNet offset (although this cannot be trusted since<br>
  it changes between WordNet versions). For most of the concepts there<br>
  is a fifth column which is a Wikipedia article related to the<br>
  concept.

<br>
* webupv13_test_lists.zip

  -&gt; test_iids.txt : IDs of the images in the test set (2000).

  -&gt; test_*urls.txt : The original URLs from where the images (iurls)<br>
       and the webpages (rurls) were downloaded. Each line in the file<br>
       corresponds to an image, starting with the IID and is followed<br>
       by one or more URLs.

       Note: These are included only to acknowledge the source of the<br>
       data, not be used as input to the annotation systems.

  -&gt; test_concepts.txt : List concepts for the test set.

  -&gt; test_gnd.txt : Ground truth concepts for the test set images.

  The definition of the concepts is the same as for<br>
  devel_concepts.txt. Note that the concepts are not the same as for<br>
  the development set.

<br>
* webupv13_baseline.zip

  An archive that includes code for computing the evaluation measures<br>
  for two baseline techniques. See the included README.txt for<br>
  details.

 

* feats_textual/webupv13_train_textual_pages.zip

  Contains all of the webpages which referenced the images in the<br>
  training set after being converted to valid xml. In total there are<br>
  262588 files, since each image can appear in more than one page, and<br>
  there can be several versions of same page which differ by the<br>
  method of conversion to xml. To avoid having too many files in a<br>
  single directory (which is an issue for some types of partitions),<br>
  the files are found in subdirectories named using the first two<br>
  characters of the RID, thus the paths of the files after extraction<br>
  are of the form:

    ./WEBUPV/pages/{RID:0:2}/{RID}.{CONVM}.xml.gz

  To be able to locate the training images withing the webpages, the<br>
  URLs of the images as referenced are provided in the file<br>
  train_rimgsrc.txt.

<br>
* feats_textual/webupv13_train_textual.scofeat.gz

  The processed text extracted from the webpages near where the images<br>
  appeared. Each line corresponds to one image, having the same order<br>
  as the train_iids.txt list. The lines start with the image ID,<br>
  followed by the number of extracted unique words and the<br>
  corresponding word-score pairs. The scores were derived taking into<br>
  account 1) the term frequency (TF), 2) the document object model<br>
  (DOM) attributes, and 3) the word distance to the image. The scores<br>
  are all integers and for each image the sum of scores is always<br>
  &lt;=100000 (i.e. it is normalized).

<br>
* feats_textual/webupv13_train_textual.keywords.gz

  The words used to find the images when querying image search<br>
  engines. Each line corresponds to an image (in the same order as in<br>
  train_iids.txt). The lines are composed of triplets:

    [keyword] [rank] [search_engine]

  where [keyword] is the word used to find the image, [rank] is the<br>
  position given to the image in the query, and [search_engine] is a<br>
  single character indicating in which search engine it was found<br>
  ('g':google, 'b':bing, 'y':yahoo).

<br>
* feats_visual/webupv13_*_images.zip

  Contains thumbnails (maximum 640 pixels of either width or height)<br>
  of the images in jpeg format. To avoid having too many files in a<br>
  single directory (which is an issue for some types of partitions),<br>
  the files are found in subdirectories named using the first two<br>
  characters of the IID, thus the paths of the files after extraction<br>
  are of the form:

    ./WEBUPV/images/{IID:0:2}/{IID}.jpg

 

* feats_visual/webupv13_*.feat.gz

  The visual features in a simple ASCII text sparse format. The first<br>
  line of the file indicates the number of vectors (N) and the<br>
  dimensionality (DIMS). Then each line corresponds to one vector,<br>
  starting with the number of non-zero elements and followed by pairs<br>
  of dimension-value, being the first dimension 0. In summary the file<br>
  format is:

    N DIMS<br>
    nz1 Dim(1,1) Val(1,1) ... Dim(1,nz1) Val(1,nz1)<br>
    nz2 Dim(2,1) Val(2,1) ... Dim(2,nz2) Val(2,nz2)<br>
    ...<br>
    nzN Dim(N,1) Val(N,1) ... Dim(N,nzN) Val(N,nzN)

  The order of the features is the same as in the lists<br>
  devel_iids.txt, test_iids.txt and train_iids.txt.

  The procedure to extract the SIFT based features in this<br>
  subdirectory was conducted as follows. Using the ImageMagick<br>
  software, the images were first rescaled to having a maximum of 240<br>
  pixels, of both width and height, while preserving the original<br>
  aspect ratio, employing the command:

    convert {IMGIN}.jpg -resize '240&gt;x240&gt;' {IMGOUT}.jpg

  Then the SIFT features where extracted using the ColorDescriptor<br>
  software from Koen van de Sande<br>
  (http://koen.me/research/colordescriptors). As configuration we<br>
  used, 'densesampling' detector with default parameters, and a hard<br>
  assignment codebook using a spatial pyramid as<br>
  'pyramid-1x1-2x2'. The number in the file name indicates the size of<br>
  the codebook. All of the vectors of the spatial pyramid are given in<br>
  the same line, thus keeping only the first 1/5th of the dimensions<br>
  would be like not using the spatial pyramid. The codebook was<br>
  generated using 1.25 million randomly selected features and the<br>
  k-means algorithm. The GIST features were extracted using the<br>
  LabelMe Toolbox. The images where first resized to 256x256 ignoring<br>
  original aspect ratio, using 5 scales, 6 orientations and 4<br>
  blocks. The other features colorhist and getlf, are both color<br>
  histogram based extracted using our own implementation.