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ImageCLEF 2008: Photo Retrieval Task

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ImageCLEF 2008 photographic retrieval task



Ad-hoc photographic retrieval task 2008

Introduction


The photo retrieval task of ImageCLEF2008 will take a different approach to evaluation by studying image clustering. A good image search engine ensures that duplicate or near duplicate documents retrieved in response to a query are hidden from the user. Ideally the top results of a ranked list will contain diverse items representing different sub-topics within the results. Providing this functionality is particularly important when a user types in a query that is either poorly specified or ambiguous; a common query in image search. Given such a query, a search engine that retrieves a diverse, yet relevant set of images at the top of a ranked list is more likely to satisfy its users [1,2].

Another reason why it's a good idea to promote diversity is because often different people type in the same query but wish to see different results. So if a search engine knows nothing about the user entering the query, a good strategy for the engine is to produce results that are both diverse (i.e. representative of all sub-topics) and relevant, effectively the engine is spreading its bets on what the user might want to retrieve.

This is an ImageCLEF task.

The Task - Promote Diversity

Participants will run each provided topic on their image search system and produce a ranking that in the top 20, holds as many relevant images that are representative of the different sub-topics within the results. The definition of what consitutes diversity will vary across the topics, but there will be a clear indication in the topic (using a new topic tag, "cluster") indicating what the clustering criteria the evaluators will use.

For each topic in the ImageCLEFPhoto set, relevant images will be manually clustered into sub-topics and relevance judgements will be augmented to indicate which cluster an image belongs to. Relevance assessors will be instructed to look for simple clusters based on the form of a topic. For example if a topic asks for images of beaches in Brazil, clusters will be formed based on location; if a topic asks for photos of animals, clusters will be formed based on animal type.

Participating groups will return to us, for each topic, a ranked list of images IDs. We will determine which images are relevant and count how many clusters are represented in the ranking. We do not require you to identify or label clusters in the ranked list how you choose to do the clustering is an internal matter for you.

Evaluation will be based on two measures: precision at 20 and instance recall at rank 20 (also called S-recall) [3], which calculates the percentage of different clusters represented in the top 20. It will be important to maximise both measures: simply getting lots of relevant images from one cluster or filling the ranking with diverse, but non-relevant images, will result in a poor overall effectiveness score.

Note, that it is quite possible to submit runs from a "standard" non-clustering image search system, though we would expect clustering systems to out-perform the standard systems in producing a diverse ranked list in the top 20.

A version of the collection will be made available that allows participants to explore cross language aspects of image clustering. In this version, members of the clusters will be captioned in two languages.

Query Topics

Topics download (note password protected).

We will use existing topic statements of past ImageCLEFPhoto years. The topic format is the same as previous years but with an additional tag, the cluster tag, which defines how the clustering of images should take place.
<top>
<num> Number: 5 </num>
<title> animals swimming </title>
<cluster>animal</cluster>
<narr> </narr>
<image> 3739.jpg </image>
<image> 4968.jpg </image>
<image> 30823.jpg </image>
</top>
Note: this year we will only offer topic languages in English.

Data Collection

The collection is composed of a set of images (full size and thumbnails) and a set of annotations. Note site password protected. Annotations from the Visual Concept Detection Task of ImageCLEF 2008 are being made available at this site.

The image collection of the IAPR TC-12 photographic collection consists of 20,000 still natural images (plus 20,000 corresponding thumbnails) taken from locations around the world and comprising an assorted cross-section of still natural images[4]. This includes pictures of different sports and actions, photographs of people, animals, cities, landscapes and many other aspects of contemporary life.

Each image is associated with an alphanumeric caption stored in a semi-structured format. These captions include the title of the image, its creation date, the location at which the photograph was taken, the name of the photographer, a semantic description of the contents of the image (as determined by the photographer) and additional notes.

<DOC>
<DOCNO>annotations/00/60.eng</DOCNO>
<TITLE>Palma </TITLE>
<DESCRIPTION>two lane street with large shops on the right and smaller shops on the left; people are walking on the sidewalk, some are crossing the street; cars are parked along the left side of the street as well; </DESCRIPTION>
<NOTES>The main shopping street in Paraguay; </NOTES>
<LOCATION>Asunción, Paraguay </LOCATION>
<DATE>March 2002 </DATE>
<IMAGE>images/00/60.jpg </IMAGE>
<THUMBNAIL>thumbnails/00/60.jpg </THUMBNAIL>
</DOC>


The collection will be available in two forms:

  • All English, where all titles, descriptions, locations, etc are written in the English language
  • A multi-lingual version, where each image is annotated in a different language. The languages used are English and German. (We hope to release this collection by the end of April.)
Further information about the image collection and links to related publications can be found here.

How to Cheat (but please don't)

There is one way to cheat at this year's ImageCLEFPhoto, we're trusting you not to do any of these:

  • Use last year's qrels: the topics this year are the same as last year, which means some of you might still have copies of last year's relevance judgements (the judgements we release this year will be clustering information on top of the old qrels). We are trusting you not use last year's relevance judgements for this year's runs.

Submission format and guidelines
Submission formats

When you submit your runs, please name your runs using the following code elements. All submissions should be sent by email to Thomas Arni (t.arni@sheffield.ac.uk).

Dimension Available Codes
Topic language EN
Annotation language EN, RND (DE, EN)
Query/run type AUTO, MAN
Modality IMG, TXT, TXTIMG
Run type MAN, AUTO
Query language
Used to specify the query language used in the run (this year English only).

Annotation language

Used to specify the target language (i.e. the annotation set) used for the run: English (EN) or random (RND).

Modality

This describes the use of visual (image) or text features in your submission. A text-only run will have modality text (TXT); a purely visual run will have modality image (IMG) and a combined submission (e.g. initial text search followed by a possibly combined visual search) will have modality text+image (TXTIMG).

Run Type

We distinguish between manual (MAN) and automatic (AUTO) submissions. Automatic runs will involve no user interaction; whereby manual runs are those in which a human has been involved in query construction and the iterative retrieval process, e.g. manual relevance feedback is performed. We encourage groups who want to investigate manual intervention further to participate in the interactive evaluation (iCLEF) task.


What to submit

Participants can submit a run in any of the permutations detailed in the previous table (above) :
  • EN-EN-AUTO-TXTIMG for the English-English monolingual run using fully automatic text and image clustering methods
  • EN-EN-MAN-TXT for the English-English monolingual run using text clustering methods using some manual intervention
  • EN-RND-AUTO-IMG for the English-Random language run using fully automated image clustering methods
It is extremely important that we can get a description of the techniques used for each submitted run. This should be as detailed as possible to ease the comparison or classification of techniques and results.

Submission format

Participants are required to submit ranked lists of (up to) the top 1000 images ranked in descending order of similarity (i.e. the highest nearer the top of the list). The format of submissions for this ad-hoc task can be found here and the filenames should distinguish different types of submission according to the table above. Participants can submit (via email) as many system runs as they require.

Please note that there should be at least 1 document entry in your results for each topic (i.e. if your system returns no results for a query then insert a dummy entry, e.g. 25 1 16/16019 0 4238 xyzT10af5 ). The reason for this is to make sure that all systems are compared with the same number of topics and relevant documents. Submissions not following the required format will not be evaluated.

Important Dates

22 April 2008: Data and Topic Release
UPDATE : Extended to 22 June 2008: Submission of retrieval runs due
15 July 2008: Release of retrieval results
15 August 2008: Workshop papers due
17-19 September 2008: CLEF workshop in Aarhus, Denmark

Organisers of ImageCLEFphoto


Primary Contact

Thomas Arni, Department of Information Studies, University of Sheffield, UK



Mark Sanderson, Department of Information Studies, University of Sheffield, UK
(m.sanderson@shef.ac.uk)


Paul Clough, Department of Information Studies, University of Sheffield, UK
(p.d.clough@sheffield.ac.uk)


Michael Grubinger, School of Computer Science and Mathematics, Victoria University, Australia


Mailing List

We have set up a mailing list: imageclef@sheffield.ac.uk for participants. Please contact Paul Clough to be added to the list.
References

[1] Chen, H. and Karger, D. R. 2006. Less is more: probabilistic models for retrieving fewer relevant documents. In Proceedings of the 29th Annual international ACM SIGIR Conference on Research and Development in information Retrieval (Seattle, Washington, USA, August 06 - 11, 2006). SIGIR '06. ACM, New York, NY, 429-436.
[2] Song, K., Tian, Y., Gao, W., and Huang, T. 2006. Diversifying the image retrieval results. In Proceedings of the 14th Annual ACM international Conference on Multimedia (Santa Barbara, CA, USA, October 23 - 27, 2006). MULTIMEDIA '06. ACM, New York, NY, 707-710.
[3] Zhai, C. X., Cohen, W. W., and Lafferty, J. 2003. Beyond independent relevance: methods and evaluation metrics for subtopic retrieval. In Proceedings of the 26th Annual international ACM SIGIR Conference on Research and Development in informaion Retrieval (Toronto, Canada, July 28 - August 01, 2003). SIGIR '03. ACM, New York, NY, 10-17.
[4] Grubinger, M., Clough, P., Müller, H. and Deselaers, T. (2006), The IAPR TC-12 Benchmark: A New Evaluation Resource for Visual Information Systems, In Proceedings of International Workshop OntoImage’2006 Language Resources for Content-Based Image Retrieval, held in conjuction with LREC'06, pages 13-23, Genoa, Italy, 22 May 2006 (pdf).



Last Modified: 15 April 2008
By: Mark Sanderson
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