DOI: 10.5281/zenodo.259758
title: 2014 Imageclef Webupv Collection

description: This document describes the WEBUPV dataset compiled for the ImageCLEF 2014<br>
Scalable Concept Image Annotation challenge. The data mentioned here indicates what<br>
is ready for download. However, upon request or depending on feedback from the<br>
participants, additional data may be released.

The following is the directory structure of the collection, and bellow there<br>
is a brief description of what each compressed file contains. The<br>
corresponding MD5 checksums of the files shown (for verifying a correct<br>
download) can be found in md5sums.txt.

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

.<br>
|<br>
|--- README.txt<br>
|--- md5sums.txt<br>
|--- webupv14_train_lists.zip<br>
|--- webupv14_train2_lists.zip<br>
|--- webupv14_devel_lists.zip<br>
|--- webupv14_test_lists.zip<br>
|--- webupv14_baseline.zip<br>
|<br>
|--- feats_textual/<br>
|      |<br>
|      |--- webupv14_{train|train2}_textual_pages.zip<br>
|      |--- webupv14_{train|train2}_textual.scofeat.gz<br>
|      |--- webupv14_{train|train2}_textual.keywords.gz<br>
|<br>
|--- feats_visual/<br>
       |<br>
       |--- webupv14_{train|train2|devel|test}_visual_images.zip<br>
       |--- webupv14_{train|train2|devel|test}_visual_gist2.feat.gz<br>
       |--- webupv14_{train|train2|devel|test}_visual_sift_1000.feat.gz<br>
       |--- webupv14_{train|train2|devel|test}_visual_csift_1000.feat.gz<br>
       |--- webupv14_{train|train2|devel|test}_visual_rgbsift_1000.feat.gz<br>
       |--- webupv14_{train|train2|devel|test}_visual_opponentsift_1000.feat.gz<br>
       |--- webupv14_{train|train2|devel|test}_visual_colorhist.feat.gz<br>
       |--- webupv14_{train|train2|devel|test}_visual_getlf.feat.gz

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

* webupv14_train{|2}_lists.zip

  The first training set ("train_*") includes images for the concepts of the<br>
  development set, whereas the second training set ("train2_*") includes<br>
  images for the concepts in the test set that are not in the development set.

  -&gt; train{|2}_iids.txt : IDs of the images (IIDs) in the training set.

  -&gt; train{|2}_rids.txt : IDs of the webpages (RIDs) in the training set.

  -&gt; train{|2}_*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{|2}_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.

* webupv14_{devel|test}_lists.zip

  -&gt; {devel|test}_conceptlists.txt : Lists per image of concepts for<br>
       annotation. Each line starts with an image ID and is followed by the<br>
       list of concepts in alphabetical order. Each ID may appear more than<br>
       once. In total there are 1940 image annotation lists for the<br>
       development set and 7291 image annotation lists for the test set. These<br>
       correspond to 1000 and 4122 unique images (IDs) for the development and<br>
       test sets, respectively.

  -&gt; {devel|test}_allconcepts.txt : Complete list of concepts for the<br>
       development/test set.

       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 adjective),<br>
       the third column is the sense number and the fourth column is the<br>
       WordNet offset (although this cannot be trusted since it changes<br>
       between WordNet versions). For most of the concepts there is a fifth<br>
       column which is a Wikipedia article related to the concept.

  -&gt; {devel|test}_groundtruth.txt : Ground truth concepts for the development<br>
       and test sets.

  -&gt; {devel|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 by one<br>
       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.

<br>
* webupv14_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.

<br>
* feats_textual/webupv14_train{|2}_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.

* feats_textual/webupv14_train{|2}_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/webupv14_train{|2}_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/webupv14_*_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/webupv14_*.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_conceptlists.txt, test_conceptlists.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.