DOI: 10.5281/zenodo.1038533
title: 2012 Imageclef Webupv Collection

description: This document describes the WEBUPV dataset compiled for the ImageCLEF<br>
2012 Scalable 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 can be<br>
released. For debugging purposes, thumbnails of the images in the<br>
dataset can be obtained from a web server using '{IID}' the image<br>
identifier:

  http://risenet.prhlt.upv.es/db/img/{IID}.jpg

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

 

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

.<br>
|<br>
|--- README.txt<br>
|--- md5sums.txt<br>
|--- webupv_train_lists.zip<br>
|--- webupv_devel_lists.zip<br>
|--- webupv_test_lists.zip<br>
|--- baseline.zip<br>
|<br>
|--- feats_textual/<br>
|      |<br>
|      |--- webupv_train_textual.rawfeat.gz<br>
|      |--- webupv_train_textual.scofeat.gz<br>
|      |--- webupv_train_textual.keywords.gz<br>
|<br>
|--- feats_visual/<br>
       |<br>
       |--- webupv_{train|devel|test}_visual_gist.feat.gz<br>
       |--- webupv_{train|devel|test}_visual_sift_*.feat.gz<br>
       |--- webupv_{train|devel|test}_visual_csift_*.feat.gz<br>
       |--- webupv_{train|devel|test}_visual_rgbsift_*.feat.gz<br>
       |--- webupv_{train|devel|test}_visual_opponentsift_*.feat.gz<br>
       |--- webupv_{train|devel|test}_visual_colorhist.feat.gz

 

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

* webupv_train_lists.zip<br>
  -&gt; train_iids.txt : IDs of the images in the training set (250000).<br>
  -&gt; train_rids.txt : IDs of the webpages in the training set.<br>
  -&gt; train_rimgsrc.txt : The URLs of the images as referenced in each<br>
                         of the webpages. This can also be useful as a<br>
                         textual feature.

<br>
* webupv_devel_lists.zip<br>
  -&gt; devel_iids.txt : IDs of the images in the development set (1000).<br>
  -&gt; devel_concepts.txt : List concepts for the development set.<br>
  -&gt; devel_gnd.txt : Ground truth concepts for the development set<br>
                     images.

<br>
* webupv_test_lists.zip<br>
  -&gt; test_iids.txt : IDs of the images in the test set (2000).<br>
  -&gt; test_concepts.txt : List concepts for the test set.<br>
  -&gt; test_gnd.txt : Ground truth concepts for the test set images.

<br>
* baseline.zip

  An archive that includes code for computing the evaluation measures<br>
  for two baseline techniques for the "Scalable concept image<br>
  annotation" subtask. See the included README.txt for details.

<br>
* feats_textual/webupv_train_textual.rawfeat.gz

  The raw text extracted from the webpages near where the images<br>
  appeared. Each line starts with the image and webpage IDs followed<br>
  by the text extracted. The position of the image within the text is<br>
  indicated by the special word '{X}'. The extracted text is somewhat<br>
  filtered (e.g. there are no HTML tags), although removed words and<br>
  tags have been replaced by full stops '.' to preserve word<br>
  distances. The title of the webpage is always included, and it is<br>
  the first sentence of the text. In total the file has 275749 lines<br>
  since the images can appear in more than one webpage.

<br>
* feats_textual/webupv_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/webupv_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<br>
  in 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/webupv_*.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.

<br>
Contact<br>
-------

For further questions, please contact:<br>
  Mauricio Villegas &lt;mauvilsa@upv.es&gt;