Dataset Restricted Access

2015 ImageCLEF WEBUPV Collection

Gilbert, Andrew; Piras, Luca; Wang, Josiah; Yan, Fei; Dellandrea, Emmanuel; Gaizauskas, Robert; Villegas, Mauricio; Mikolajczyk, Krystian

This document describes the WEBUPV dataset compiled for the ImageCLEF 2015
Scalable Concept Image Annotation challenge. The data mentioned here indicates
what is ready for download. However, upon request or depending on feedback
from the 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
download) can be found in md5sums.txt.


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

.
|
|--- README.txt
|--- md5sums.txt
|--- webupv15_data_lists.zip
|
|--- annotations/imageclef2015.dev.bbox.*.gz
|--- annotations/imageclef2015.dev.textdesc.*.gz
|--- annotations/imageclef2015.subtask2cleantrack.*.input_bbox.*.gz
|--- annotations/imageclef2015.subtask2cleantrack.dev.textdesc.*.gz
|--- annotations/imageclef2015.dev.bbox.supplement.*.gz
|--- annotations/imageclef2015.hierarchy.*.gz
|--- annotations/imageclef2015.concept_to_parents.*.gz
|
|--- feats_textual/
|      |
|      |--- webupv15_data_textual_pages.zip
|      |--- webupv15_data_textual.scofeat.gz
|      |--- webupv15_data_textual.keywords.gz
|
|--- feats_visual/
       |
       |--- webupv15_data_visual_images.zip
       |--- webupv15_data_visual_gist.dfeat.gz
       |--- webupv15_data_visual_sift_1000.sfeat.gz
       |--- webupv15_data_visual_csift_1000.sfeat.gz
       |--- webupv15_data_visual_rgbsift_1000.sfeat.gz
       |--- webupv15_data_visual_opponentsift_1000.sfeat.gz
       |--- webupv15_data_visual_colorhist.sfeat.gz
       |--- webupv15_data_visual_getlf.sfeat.gz
       |--- webupv15_data_visual_vgg16-fc8.dfeat.gz


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

* annotations/imageclef2015.dev.bbox.*.gz

  Development set ground truth localised annotations for sub task 1.

  The format for the development set of annotated bounding boxes of
  the concepts is

  <image_ID> <seq> <Concept>  <confidence> <xmin> <ymin> <xmax> <ymax>

  The development set contains 1,979 images. The bounding boxes may enclose
  single instances (a single tree) or grouped instances (e.g. group of trees),
  depending on the context. The annotations are not exhaustive: the emphasis
  is on concepts that are interesting enough to be described in the image,
  although background objects are also optionally annotated by our annotators
  in many cases. Also note that a person might not be annotated if the
  annotator could not decide whether the person is a man/woman/boy/girl.


* annotations/imageclef2015.dev.textdesc.*.gz

  Development set ground truth textual description annotations of images for
  sub task 2

  The format is:

  <image_ID> \t <text_description_seq> \t <textual_description>

  The development set contains 2,000 images with 5 to 51 textual descriptions
  per image (mean: 9.492, median: 8). Please note that the sentences contain a
  mix of both American and British English spelling variants (e.g. color vs
  colour) -- we have decided to retain this variation in the annotations to
  reflect the challenge of real-world English spelling variants. Basic
  spell-correction has been performed on the textual descriptions, but we cannot
  guarantee that they are completely free from spelling or grammatical error.

  Changelog for v20150306: We have removed some poor textual descriptions that
  managed to slip past our quality control check. Please use this latest version
  (v20150306) for your development needs.


* annotations/imageclef2015.subtask2cleantrack.*.input_bbox.*.gz

  Input bounding boxes for the clean track of SubTask 2. This is just a selected
  subset of 500 development images from imageclef2015.dev.bbox above (please refer
  to above for file format).


* annotations/imageclef2015.subtask2cleantrack.dev.textdesc.*.gz

  Annotated textual descriptions for 500 development images, to be used to evaluate
  the content selection ability of the text generation system in the clean track of
  SubTask 2. The format is the same as the original imageclef2015.dev.textdesc
  file, except that we further annotated textual terms with their corresponding
  input bounding boxes, for example [[[dogs|0,4]]] in a textual description refers
  to the two instances of dogs with the bounding box id 0 and 4 in
  imageclef2015.subtask2cleantrack.dev.input_bbox.

  Note that not all descriptions from the original imageclef2015.dev.textdesc are
  used in this version, and as such the sequence numbers of the descriptions may not
  necessarily be contiguous as we retained the sequence numbers from the original
  file for consistency.


* annotations/imageclef2015.dev.bbox.supplement.*.gz

  This file contains all bounding boxes from imageclef2015.dev.bbox, and additional
  bounding boxes for additional 'general level' categories ('building', 'person',
  'animal' etc.) not in the 251 concepts list. These special categories are prefixed
  with an asterisk(*). We hope that you will find these useful for your development
  purposes.


* annotations/imageclef2015.hierarchy.*.gz

  The hierarchy structure of the 'general level' categories. File format:

  *category \t *parent-category \t definition.

  For example, *mammal is the child of *animal. '#' represents the root node.


* annotations/imageclef2015.concept_to_parents.*.gz

  List of 'general level' category parent(s) for each 251 concept. A concept may
  have multiple parents (separated by commas). File format:

  category \t *parent1,*parent2


* annotations/imageclef2015.test.groundtruth.zip

 

  Ground truth for the test.

 

 
* webupv15_data_lists.zip

  -> data_iids.txt : IDs of the images (IIDs) in the dataset.

  -> data_rids.txt : IDs of the webpages (RIDs) in the dataset.

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

  -> data_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.

    
* feats_textual/webupv15_data_textual_pages.zip

  Contains all of the webpages which referenced the images in the
  dataset set after being converted to valid xml. In total there are
  515754 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 images withing the webpages, the URLs of the
  images as referenced are provided in the file data_rimgsrc.txt.


* feats_textual/webupv15_data_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
  as the data_iids.txt list. The lines start with the image ID,
  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
  <=100000 (i.e. it is normalized).


* feats_textual/webupv15_data_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
  data_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
  ('g':google, 'b':bing, 'y':yahoo).


* feats_visual/webupv15_data_visual_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/webupv15_*.{s|d}feat.gz

  The visual features in a simple ASCII text format either in sparse
  (*.sfeat.gz files) or dense (*.dfeat.gz files). The first
  line of the file indicates the number of vectors (N) and the
  dimensionality (DIMS). Then each line corresponds to one vector.
  For the dense features each line has exactly DIMS values separated
  by spaces, i.e., the format is:

    N DIMS
    Val(1,1) Val(1,2) ... Val(1,DIMS)
    Val(2,1) Val(1,2) ... Val(2,DIMS)
    ...
    Val(N,1) Val(N,2) ... Val(N,DIMS)

  For the sparse features, each line starts with the number of non-zero
  elements and is followed by dimension-value pairs, being the first
  dimension 0, i.e., the 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 list data_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>x240>' {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.


* webupv15_data_visual_vgg16-relu7.dfeat.gz

  Contains the 4096 dimensional activations of the relu7 layer of Oxford
  VGG’s 16-layer CNN model, extracted using the Berkeley Caffe library.
  More details can be found at https://github.com/BVLC/caffe/wiki/Model-Zoo.


* webupv15_data_visual_vgg16-fc8.dfeat.gz

  Contains the 1000 dimensional activations of the fc8 layer of Oxford
  VGG’s 16-layer CNN model, extracted using the Berkeley Caffe library.
  More details can be found at https://github.com/BVLC/caffe/wiki/Model-Zoo.
  The 1000 dimensions correspond to the 1000 categories in
  webupv15_data_visual_vgg16-fc8_categories.lst: the higher the value
  in the 1000d feature, the more likely the corresponding synset appears
  in the image.


Contact
-------

For further questions, please contact:
  Mauricio Villegas <mauvilsa@upv.es>
  Fei Yan <f.yan@surrey.ac.uk>

 

Restricted Access

You may request access to the files in this upload, provided that you fulfil the conditions below. The decision whether to grant/deny access is solely under the responsibility of the record owner.


This dataset is available under a Creative Commons Attribution-
NonCommercial-ShareAlike 3.0 Unported License. Before downloading
the data, please read and accept the Creative Commons License and
the following usage agreement:

Data Usage Agreement ImageCLEF 2012/2013/2014/2015/2016 WEBUPV Image
Annotation Datasets

By downloading the "Dataset", you (the "Researcher") agrees to the
following terms.

* The Researcher will only use the Dataset for non-commercial
research and/or educational purposes.

* The Researcher will cite one of the following papers in any
publication that makes use of the Dataset.

  Gilbert, A., Piras, L., Wang, J., Yan, F., Ramisa, A.,
  Dellandrea, E., Gaizauskas, R., Villegas, M., Mikolajczyk, K.:
  Overview of the ImageCLEF 2016 scalable concept image
  annotation task. In: CLEF2016 Working Notes, CEUR Workshop
  Proceedings, CEUR-WS.org, Évora, Portugal, 5–8 September 2016

  Gilbert, A., Piras, L., Wang, J., Yan, F., Dellandrea, E.,
  Gaizauskas, R., Villegas, M., Mikolajczyk, K.: Overview of the
  ImageCLEF 2015 Scalable Image Annotation, Localization and
  Sentence Generation task. In: CLEF2015 Working Notes. CEUR
  Workshop Proceedings, CEUR-WS.org, Toulouse, France (September
  8-11 2015)

  Villegas, M., Paredes, R.: Overview of the ImageCLEF 2014
  Scalable Concept Image Annotation Task. In: CLEF2014 Working
  Notes. CEUR Workshop Proceedings, vol. 1180, pp. 308–328.
  CEUR-WS.org, Sheffield, UK (September 15-18 2014)

  Villegas, M., Paredes, R., Thomee, B.: Overview of the ImageCLEF
  2013 Scalable Concept Image Annotation Subtask. In: CLEF 2013
  Evaluation Labs and Workshop, Online Working Notes. Valencia,
  Spain (September 23-26 2013)

* The Researcher may provide research associates and colleagues a
copy of the Dataset provided that they also agree to this Data
Usage Agreement.

* The Researcher will assume all responsibility against any claims
arising from Researcher's use of the Dataset.


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