Dataset Restricted Access

# 2014 ImageCLEF WEBUPV Collection

Villegas, Mauricio; Paredes, Roberto

### Dublin Core Export

<?xml version='1.0' encoding='utf-8'?>
<oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
<dc:creator>Villegas, Mauricio</dc:creator>
<dc:creator>Paredes, Roberto</dc:creator>
<dc:date>2014-04-01</dc:date>
<dc:description>This document describes the WEBUPV dataset compiled for the ImageCLEF 2014
Scalable Concept Image Annotation challenge. The data mentioned here indicates what
participants, additional data may be released.

The following is the directory structure of the collection, and bellow there
is a brief description of what each compressed file contains. The
corresponding MD5 checksums of the files shown (for verifying a correct

Directory structure
-------------------

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

Contents of files
-----------------

* webupv14_train{|2}_lists.zip

The first training set ("train_*") includes images for the concepts of the
development set, whereas the second training set ("train2_*") includes
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)
corresponds to an image, starting with the IID and is followed
by one or more URLs.

-&gt; train{|2}_rimgsrc.txt : The URLs of the images as referenced in each
of the webpages. Each line of the file is of the form: IID RID
URL1 [URL2 ...]. This information is necessary to locate the
images within the webpages and it can also be useful as a
textual feature.

* webupv14_{devel|test}_lists.zip

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

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

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

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

-&gt; {devel|test}_*urls.txt : The original URLs from where the images (iurls)
corresponds to an image, starting with the IID and is followed by one
or more URLs.

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

* webupv14_baseline.zip

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

* feats_textual/webupv14_train{|2}_textual_pages.zip

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

* feats_textual/webupv14_train{|2}_textual.scofeat.gz

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

* feats_textual/webupv14_train{|2}_textual.keywords.gz

The words used to find the images when querying image search
engines. Each line corresponds to an image (in the same order as 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
position given to the image in the query, and [search_engine] is a
single character indicating in which search engine it was found

* feats_visual/webupv14_*_images.zip

Contains thumbnails (maximum 640 pixels of either width or height)
of the images in jpeg format. To avoid having too many files in a
single directory (which is an issue for some types of partitions),
the files are found in subdirectories named using the first two
characters of the IID, thus the paths of the files after extraction
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
line of the file indicates the number of vectors (N) and the
dimensionality (DIMS). Then each line corresponds to one vector,
starting with the number of non-zero elements and followed by pairs
of dimension-value, being the first dimension 0. In summary the file
format is:

N DIMS
nz1 Dim(1,1) Val(1,1) ... Dim(1,nz1) Val(1,nz1)
nz2 Dim(2,1) Val(2,1) ... Dim(2,nz2) Val(2,nz2)
...
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
devel_conceptlists.txt, test_conceptlists.txt and train_iids.txt.

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

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

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

</dc:description>
<dc:identifier>https://zenodo.org/record/259758</dc:identifier>
<dc:identifier>10.5281/zenodo.259758</dc:identifier>
<dc:identifier>oai:zenodo.org:259758</dc:identifier>
<dc:relation>info:eu-repo/grantAgreement/EC/FP7/600707/</dc:relation>
<dc:relation>url:http://ceur-ws.org/Vol-1180/CLEF2014wn-Image-VillegasEt2014.pdf</dc:relation>
<dc:relation>url:http://imageclef.org/2014/annotation</dc:relation>
<dc:relation>url:https://zenodo.org/communities/ecfunded</dc:relation>
<dc:relation>url:https://zenodo.org/communities/imageclef</dc:relation>
<dc:rights>info:eu-repo/semantics/restrictedAccess</dc:rights>
<dc:title>2014 ImageCLEF WEBUPV Collection</dc:title>
<dc:type>info:eu-repo/semantics/other</dc:type>
<dc:type>dataset</dc:type>
</oai_dc:dc>

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