Skip to main content
SpringerLink
Log in

Multimedia Resource Discovery

  • Chapter
Advanced Topics in Information Retrieval

Part of the book series: The Information Retrieval Series ((INRE,volume 33))

  • 1877 Accesses

Abstract

This chapter examines the challenges and opportunities of Multimedia Information Retrieval and corresponding search engine applications. Computer technology has changed our access to information tremendously: We used to search authors or titles (which we had to know) in library cards in order to locate relevant books; now we can issue keyword searches within the full text of whole book repositories in order to identify authors, titles and locations of relevant books. What about the corresponding challenge of finding multimedia by fragments, examples and excerpts? Rather than asking for a music piece by artist and title, can we hum its tune to find it? Can doctors submit scans of a patient to identify medically similar images of diagnosed cases in a database? Can your mobile phone take a picture of a statue and tell you about its artist and significance via a service that it sends this picture to?

In an attempt to answer some of these questions we get to know basic concepts of multimedia resource discovery technologies for a number of different query and document types: piggy-back text search, i.e., reducing the multimedia to pseudo text documents; automated annotation of visual components; content-based retrieval where the query is an image; and fingerprinting to match near duplicates.

Some of the research challenges are given by the semantic gap between the simple pixel properties computers can readily index and high-level human concepts; related to this is an inherent technological limitation of automated annotation of images from pixels alone. Other challenges are given by polysemy, i.e., the many meanings and interpretations that are inherent in visual material and the corresponding wide range of a user’s information need.

This chapter demonstrates how these challenges can be tackled by automated processing and machine learning and by utilising the skills of the user, for example through browsing or through a process that is called relevance feedback, thus putting the user at centre stage. The latter is made easier by “added value” technologies, exemplified here by summaries of complex multimedia objects such as TV news, information visualisation techniques for document clusters, visual search by example, and methods to create browsable structures within the collection.

This book chapter is an updated re-print of Rüger (2009), Multimedia resource discovery, in Göker and Davies (eds), Information Retrieval: Searching in the 21st Century, pp. 39–62, Wiley, with excerpts from Rüger (2010), Multimedia information retrieval, Lecture notes in the series Synthesis Lectures on Information Concepts, Retrieval, and Services, Morgan and Claypool Publishers, http://dx.doi.org/10.2200/S00244ED1V01Y200912ICR010.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
eBook
USD 16.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    See http://dir.yahoo.com/.

  2. 2.

    See http://www.mkweb.co.uk/places_to_visit/displayarticle.asp?id=411 accessed Aug 2010.

  3. 3.

    See http://www.oclc.org/worldcat/statistics accessed Aug 2010.

  4. 4.

    See http://flickr.com.

  5. 5.

    See http://www.youtube.com.

  6. 6.

    See http://www.apple.com/itunes.

  7. 7.

    See http://del.icio.us.

  8. 8.

    See http://www.behold.cc.

  9. 9.

    Topic 124 of TRECVid 2003, see http://www-nlpir.nist.gov/projects/tv2003.

  10. 10.

    See http://www.chlt.org.

  11. 11.

    See http://www.flickr.com.

  12. 12.

    See http://del.icio.us.

  13. 13.

    See http://plasma.nationalgeographic.com/map-machine/ as of January 2011.

References

  • Aggarwal C, Yu P (2000) The IGrid index: Reversing the dimensionality curse for similarity indexing in high dimensional space. In: Proceedings of the ACM Conference on Knowledge Discovery and Data Mining, pp 119–129

    Chapter  Google Scholar 

  • Ankerst M, Keim D, Kriegel H (1996) Circle segments: A technique for visually exploring large multidimensional data sets. http://nbn-resolving.de/urn:nbn:de:bsz:352-opus-70761, visited on February, 2011

  • Aslam J, Montague M (2001) Models for metasearch. In: Proceedings of the ACM Conference on Research and Development in Information Retrieval. ACM Press, New York, NY, pp 276–284

    Google Scholar 

  • Au P, Carey M, Sewraz S, Guo Y, Rüger S (2000) New paradigms in information visualisation. In: Proceedings of the ACM Conference on Research and Development in Information Retrieval. ACM Press, New York, NY, pp 307–309

    Google Scholar 

  • Baillie M, Jose JM (2004) An audio-based sports video segmentation and event detection algorithm. In: Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition, pp 110–110

    Google Scholar 

  • Bainbridge D, Browne P, Cairns P, Rüger S, Xu LQ (2005) Managing the growth of multimedia digital content. ERCIM News: special theme on Multimedia Informatics 16–17

    Google Scholar 

  • Bartell B, Cottrell G, Belew R (1994) Automatic combination of multiple ranked retrieval systems. In: Proceedings of the ACM Conference on Research and Development in Information Retrieval. ACM Press, New York, NY, pp 173–181

    Google Scholar 

  • Beis J, Lowe D (1997) Shape indexing using approximate nearest-neighbour search in high-dimensional spaces. In: Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition, pp 1000–1006

    Google Scholar 

  • Birmingham W, Dannenberg R, Pardo B (2006) Query by humming with the VocalSearch system. Communications of the ACM 49:49–52

    Article  Google Scholar 

  • Blei DM, Jordan M (2003) Modeling annotated data. In: Proceedings of the ACM Conference on Research and Development in Information Retrieval. ACM Press, New York, NY, pp 127–134

    Google Scholar 

  • Börner K (2000) Visible threads: A smart VR interface to digital libraries. In: Proceedings of the International Symposium on Electronic Imaging: Visual Data Exploration and Analysis, pp 228–237

    Google Scholar 

  • Campbell I (2000) Interactive evaluation of the ostensive model using a new test collection of images with multiple relevance assessments. Journal of Information Retrieval 2:89–114. http://portal.acm.org/citation.cfm?id=593954.593979, visited on December, 2010

    Article  Google Scholar 

  • Cano P, Batlle E, Kalker T, Haitsma J (2005) A review of audio fingerprinting. Journal of VLSI Signal Processing 41:271–284

    Article  Google Scholar 

  • Card S (1996) Visualizing retrieved information: A survey. IEEE Computer Graphics and Applications 16:63–67

    Article  Google Scholar 

  • Carey M, Heesch D, Rüger S (2003) Info navigator: A visualization interface for document searching and browsing. In: Proceedings of the International Conference on Distributed Multimedia Systems, pp 23–28

    Google Scholar 

  • Cavallaro A, Ebrahimi T (2004) Interaction between high-level and low-level image analysis for semantic video object extraction. Journal on Applied Signal Processing 786–797

    Google Scholar 

  • Chawda B, Craft B, Cairns P, Rüger S, Heesch D (2005) Do “attractive things work better”? An exploration of search tool visualisations. In: Proceedings of the Australasian Database Interaction Conference, vol 2, pp 46–51

    Google Scholar 

  • Christel M, Warmack A (2001) The effect of text in storyboards for video navigation. In: Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing, pp 1409–1412

    Google Scholar 

  • Christel M, Hauptmann A, Warmack A, Crosby S (1999) Adjustable filmstrips and skims as abstractions for a digital video library. In: Proceedings of the IEEE Forum on Research and Technology Advances in Digital Libraries, pp 98–104

    Chapter  Google Scholar 

  • Cockburn A, Savage J (2003) Comparing speed-dependent automatic zooming with traditional scroll and pan and zoom methods. In: Proceedings of the Australasian Database Interaction Conference, pp 87–102

    Google Scholar 

  • Cox K (1992) Information retrieval by browsing. In: Proceedings of the International Conference on New Information Technology, pp 69–80

    Google Scholar 

  • Cox K (1995) Searching through browsing. PhD thesis, University of Canberra

    Google Scholar 

  • Cox I, Miller M, Minka T, Papathomas T, Yianilos P (2000) The Bayesian image retrieval system and PicHunter. IEEE Transactions on Image Processing 9:20–38

    Article  Google Scholar 

  • Crane G (2005) Perseus digital library project. Tech rep, Tufts University. http://www.perseus.tufts.edu, visited on December, 2010

  • Croft B, Parenty T (1985) A comparison of a network structure and a database system used for document retrieval. Information Systems 10:377–390

    Article  Google Scholar 

  • Cunningham H (2002) GATE: a general architecture for text engineering. Computers and the Humanities 36:223–254

    Article  Google Scholar 

  • Datta R, Joshi D, Li J, Wang J (2008) Image retrieval: Ideas, influences and trends of the new age. ACM Computing Surveys 40:1–60

    Article  Google Scholar 

  • de Vries A, Mamoulis N, Nes N, Kersten M (2002) Efficient k-nn search on vertically decomposed data. In: Proceedings of the ACM Conference on Management of Data, pp 322–333

    Google Scholar 

  • Dearholt D, Schvaneveldt R (1990) Properties of Pathfinder networks. In: Schvaneveldt R (ed) Pathfinder Associative Networks: Studies in Knowledge Organization. Norwood, pp 1–30

    Google Scholar 

  • Doraisamy S, Rüger S (2003) Robust polyphonic music retrieval with n-grams. Journal of Intelligent Information Systems 21:53–70

    Article  Google Scholar 

  • Duygulu P, Barnard K, de Freitas N, Forsyth D (2002) Object recognition as machine translation: Learning a lexicon for a fixed image vocabulary. In: Proceedings of the European Conference on Computer Vision. Lecture Notes in Computer Science, vol 2353. Springer, Berlin, pp 349–354

    Google Scholar 

  • Enser P, Sandom C (2002) Retrieval of archival moving imagery—CBIR outside the frame? In: Proceedings of the International Conference on Image and Video Retrieval. Lecture Notes in Computer Science, vol 2383. Springer, Berlin, pp 85–106

    Google Scholar 

  • Enser P, Sandom C (2003) Towards a comprehensive survey of the semantic gap in visual image retrieval. In: Proceedings of the International Conference on Image and Video Retrieval. Lecture Notes in Computer Science, vol 2728. Springer, Berlin, pp 163–168

    Google Scholar 

  • Feng S, Manmatha R, Lavrenko V (2004) Multiple Bernoulli relevance models for image and video annotation. In: Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition, pp 1002–1009

    Google Scholar 

  • Fowler R, Wilson B, Fowler W (1992) Information navigator: An information system using associative networks for display and retrieval. Tech Rep NAG9-551, 92-1, Department of Computer Science and University of Texas

    Google Scholar 

  • Hare J, Lewis P (2004) Salient regions for query by image content. In: Proceedings of the International Conference on Image and Video Retrieval. Lecture Notes in Computer Science, vol 3115. Springer, Berlin, pp 264–268

    Google Scholar 

  • Hare J, Lewis P (2005) Saliency-based models of image content and their application to auto-annotation by semantic propagation. http://eprints.ecs.soton.ac.uk/id/eprint/10954, visited on February, 2011

  • Hare J, Lewis P, Enser P, Sandom C (2006) Mind the gap: Another look at the problem of the semantic gap in image retrieval. In: Multimedia Content Analysis and Management and Retrieval. Lecture Notes in Computer Science, vol 6073, Springer, Berlin, pp 1–12

    Google Scholar 

  • Heesch D (2005) The NNk technique for image searching and browsing. PhD thesis, Imperial College. London

    Google Scholar 

  • Heesch D, Rüger S (2003) Relevance feedback for content-based image retrieval: What can three mouse clicks achieve? In: Proceedings of the European Conference on Information Retrieval. Lecture Notes in Computer Science, vol 2633. Springer, Berlin, pp 363–376

    Google Scholar 

  • Heesch D, Rüger S (2004) Approaching the problem of multi-lingual information retrieval and visualization in Greek and Latin and Old Norse texts. In: Proceedings of the European Conference on Information Retrieval. Lecture Notes in Computer Science, vol 2997. Springer, Berlin, pp 253–266

    Google Scholar 

  • Heesch D, Pickering M, Rüger S, Yavlinsky A (2003) Video retrieval using search and browsing with key frames. http://www-nlpir.nist.gov/projects/tvpubs/tvpapers04/imperial.pdf, visited on February, 2011

  • Hemmje M, Kunkel C, Willet A (1994) LyberWorld—a visualization user interface supporting fulltext retrieval. In: Proceedings of the ACM Conference on Research and Development in Information Retrieval. ACM Press, New York, NY, pp 249–259

    Google Scholar 

  • Hoffman P, Grinstein G, Pinkney D (1999) Dimensional anchors: A graphic primitive for multidimensional multivariate information visualizations. In: Proceedings of the New Paradigms in Information Visualisation and Manipulation Workshop at ACM Conference on Information and Knowledge Management, pp 9–16

    Google Scholar 

  • Howarth P, Rüger S (2005) Trading precision for speed: Localised similarity functions. In: Proceedings of the International Conference on Image and Video Retrieval. Lecture Notes in Computer Science, vol 3568. Springer, Berlin, pp 415–424

    Chapter  Google Scholar 

  • Ishikawa Y, Subramanya R, Faloutsos C (1998) MindReader: Querying databases through multiple examples. In: Proceedings of the International Conference on Very Large Databases, pp 218–227

    Google Scholar 

  • Jeon J, Lavrenko V, Manmatha R (2003) Automatic image annotation and retrieval using cross-media relevance models. In: Proceedings of the ACM Conference on Research and Development in Information Retrieval. ACM Press, New York, NY, pp 119–126

    Google Scholar 

  • Korfhage R (1991) To see or not to see—is that the query? In: Proceedings of the ACM Conference on Research and Development in Information Retrieval. ACM Press, New York, NY, pp 134–141

    Google Scholar 

  • Lavrenko V, Manmatha R, Jeon J (2003) A model for learning the semantics of pictures. In: Neural Information Processing Systems, pp 553–560

    Google Scholar 

  • Lew M, Sebe N, Djeraba C, Jain R (2006) Content-based multimedia information retrieval: State of the art and challenges. ACM Transactions on Multimedia Computing and Communications and Applications 2:1–19

    Article  Google Scholar 

  • Liu C, Yuen J, Torralba A (2009c) Nonparametric scene parsing: Label transfer via dense scene alignment. In: Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition, pp 1972–1979

    Google Scholar 

  • Magalhães J, Rüger S (2006) Logistic regression of semantic codebooks for semantic image retrieval. In: Proceedings of the International Conference on Image and Video Retrieval. Lecture Notes in Computer Science, vol 4071. Springer, Berlin, pp 41–50

    Chapter  Google Scholar 

  • Magalhães J, Rüger S (2007) Information-theoretic semantic multimedia indexing. In: Proceedings of the International Conference on Image and Video Retrieval, pp 619–626

    Google Scholar 

  • Makadia A, Pavlovic V, Kumar S (2008) A new baseline for image annotation. In: Proceedings of the European Conference on Computer Vision. Lecture Notes in Computer Science, vol 5304. Springer, Berlin, pp 316–329

    Google Scholar 

  • Metzler D, Manmatha R (2004) An inference network approach to image retrieval. In: Proceedings of the International Conference on Image and Video Retrieval. Lecture Notes in Computer Science, vol 3115. Springer, Berlin, pp 42–50

    Chapter  Google Scholar 

  • Müller W, Henrich A (2004) Faster exact histogram intersection on large data collections using inverted VA-files. In: Proceedings of the International Conference on Image and Video Retrieval. Lecture Notes in Computer Science, vol 3115. Springer, Berlin, pp 455–463

    Chapter  Google Scholar 

  • Nene S, Nayar S (1997) A simple algorithm for nearest neighbor search in high dimensions. IEEE Transactions on Pattern Analysis and Machine Intelligence 19:989–1003

    Article  Google Scholar 

  • Pickering M (2004) Video retrieval and summarisation. PhD thesis, Imperial College London

    Google Scholar 

  • Pickering M, Wong L, Rüger S (2003) ANSES: Summarisation of news video. In: Proceedings of the International Conference on Image and Video Retrieval. Lecture Notes in Computer Science, vol 2728. Springer, Berlin, pp 481–486

    Google Scholar 

  • Rodden K, Basalaj W, Sinclair D, Wood K (1999) Evaluating a visualization of image similarity. In: Proceedings of the ACM Conference on Research and Development in Information Retrieval. ACM Press, New York, NY, pp 36–43

    Google Scholar 

  • Rüger S (2009) Multimedia resource discovery. In: Göker A, Davies J (eds) Information Retrieval: Searching in the 21st Century. Wiley, New York, NY, pp 39–62

    Google Scholar 

  • Rüger S (2010) Multimedia Information Retrieval. Morgan & Claypool

    Google Scholar 

  • Rui Y, Huang T, Mehrotra S (1998) Relevance feedback techniques in interactive content-based image retrieval. In: Multimedia Content Analysis and Management and Retrieval, pp 25–36

    Google Scholar 

  • Rydberg-Cox J, Vetter L, Rüger S, Heesch D (2004) Approaching the problem of multi-lingual information retrieval and visualization in Greek and Latin and Old Norse texts. In: Proceedings of the European Conference on Digital Libraries. Lecture Notes in Computer Science, vol 3232. Springer, Berlin, pp 168–178

    Google Scholar 

  • Salway A, Graham M (2003) Extracting information about emotions in films. In: Proceedings of the ACM Conference on Multimedia, pp 299–302

    Google Scholar 

  • Salway A, Vassiliou A, Ahmad K (2005) What happens in films? http://doi.ieeecomputersociety.org/10.1109/ICME.2005.1521357, visited on February, 2011

  • Sammon J (1969) A nonlinear mapping for data structure analysis. IEEE Transactions on Computers 18:401–409

    Article  Google Scholar 

  • Santini S, Jain R (2000) Integrated browsing and querying for image databases. IEEE Multimedia 7:26–39

    Article  Google Scholar 

  • Seo J, Haitsma J, Kalker T, Yoo C (2004) A robust image fingerprinting system using the radon transform. Signal Processing: Image Communication 19:325–339

    Article  Google Scholar 

  • Shaw J, Fox E (1994) Combination of multiple searches. In: Proceedings of the Text Retrieval Conference, pp 243–252

    Google Scholar 

  • Shneiderman B, Feldman D, Rose A, Ferré Grau X (2000) Visualizing digital library search results with categorical and hierarchical axes. In: Proceedings of the ACM Conference on Digital Libraries, pp 57–66

    Chapter  Google Scholar 

  • Smeaton A, Gurrin C, Lee H, Mc Donald K, Murphy N, O’Connor N, O’Sullivan D, Smyth B, Wilson D (2004) The Físchlár-news-stories system: Personalised access to an archive of TV news. In: Acte de la Conférence sur la Recherche d’Information Assistée par Ordinateur, pp 3–17

    Google Scholar 

  • Squire D, Müller W, Müller H, Pun T (2000) Content-based query of image databases: Inspirations from text retrieval. Pattern Recognition Letters 21:1193–1198

    Article  MATH  Google Scholar 

  • Tolonen T, Karjalainen M (2000) A computationally efficient multi-pitch analysis model. IEEE Transactions on Speech and Audio Processing 8:708–716

    Article  Google Scholar 

  • Torralba A, Oliva A (2003) Statistics of natural image categories. Network: Computation in Neural Systems 14:391–412

    Article  Google Scholar 

  • TRECVid (2003) TREC video retrieval evaluation. http://trecvid.nist.gov/, visited on December, 2010

  • Tzanetakis G, Cook P (2002) Musical genre classification of audio signals. IEEE Transactions on Speech and Audio Processing 10:293–302

    Article  Google Scholar 

  • van Dongen S (2000) A cluster algorithm for graphs. Tech Rep INS-R0010, National Research Institute for Mathematics and Computer Science in the Netherlands

    Google Scholar 

  • von Ahn L, Dabbish L (2004) Labeling images with a computer game. In: Proceedings of the ACM Conference on Human Factors in Computing Systems, pp 319–326

    Chapter  Google Scholar 

  • Voss J (2007) Tagging, folksonomy & Co—Renaissance of manual indexing? Computing Research Repository abs/cs/0701072:1–12

    Google Scholar 

  • Watts D, Strogatz S (1998) Collective dynamics of ‘small-world’ networks. Nature 393:440–442

    Article  Google Scholar 

  • Weber R, Stock H, Blott S (1998) A quantitative analysis and performance study for similarity search methods in high-dimensional space. In: Proceedings of the International Conference on Very Large Databases, pp 194–205

    Google Scholar 

  • Wood M, Campbell N, Thomas B (1998) Iterative refinement by relevance feedback in content-based digital image retrieval. ACM Multimedia 13–20

    Google Scholar 

  • Yavlinsky A, Pickering M, Heesch D, Rüger S (2004) A comparative study of evidence combination strategies. In: Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing, pp 1040–1043

    Google Scholar 

  • Yavlinsky A, Schofield E, Rüger S (2005) Automated image annotation using global features and robust nonparametric density estimation. In: Proceedings of the International Conference on Image and Video Retrieval. Lecture Notes in Computer Science, vol 3568. Springer, Berlin, pp 507–517

    Chapter  Google Scholar 

Download references

Acknowledgements

Outlining the paradigms in this chapter and their implementations would not have been possible without the ingenuity, imagination and hard work of Paul Browne, Matthew Carey, Shyamala Doraisamy, Daniel Heesch, Peter Howarth, Suzanne Little, Haiming Liu, Ainhoa Llorente, João Magalhães, Alexander May, Simon Overell, Marcus Pickering, Adam Rae, Edward Schofield, Shalini Sewraz, Dawei Song, Lawrence Wong and Alexei Yavlinsky.

Credits

The photograph in Fig. 7.1 (Milton Keynes Peace pagoda) by Stefan Rüger, July 2007, was first published in Rüger (2010). Figure 7.2 is a mock-up based on the existing üBase search engine, see Fig. 7.14, with modifications by Peter Devine and was previously published in Rüger (2010). Figure 7.3 (new search engine types) was designed by Peter Devine and published in Rüger (2010). Figures 7.5, 7.6 and 7.9 use royalty-free images from Corel Gallery 380,000, © Corel Corporation, all rights reserved. Figure 7.7 (Behold) by Alexei Yavlinsky are screenshots from http://photo.beholdsearch.com, 19 July 2007, now http://www.behold.cc with thumbnails of creative-commons Flickr images. The photograph in Fig. 7.8 © by Stefan Rüger, taken May 1996 in the Nord Jyllands Kunstmuseum, Ålborg. Figures 7.8 and 7.9 were published in Rüger (2010). The screenshots in Figs. 7.10–7.14 and 7.16–7.18 are reproduced courtesy of © Imperial College London. The ANSES system in Fig. 7.10 was originally designed by Marcus Pickering and later modified by Lawrence Wong; the images and part of the text displayed in the screenshot of Fig. 7.10 were recorded from British Broadcasting Corporation (BBC), http://www.bbc.co.uk. The Sammon map in Fig. 7.11 and the radial visualisation in Fig. 7.13 were designed by Matthew Carey. The Dendro map in Fig. 7.12 was designed by Daniel Heesch. The üBase system depicted in the screenshots of Figs. 7.14(a), 7.14(b) and 7.16(a) was designed by Alexander May. The images used within the screenshot of Fig. 7.14 and within the illustration of Fig. 7.15 were reproduced from Corel Gallery 380,000, © Corel Corporation, all rights reserved. The images in the (partial) screenshots of Figs. 7.16 and 7.17 were reproduced from TREC Video Retrieval Evaluation 2003 (TRECVid), http://www-nlpir.nist.gov/projects. The geotemporal browsing screenshot in Fig. 7.18 was created by Simon Overell.

Fig. 7.12
figure 12

Dendro Map—A plane-spanning binary tree (query “Beethoven”)

Full size image
Fig. 7.13
figure 13

Radial visualisation

Full size image
Fig. 7.14
figure 14

Visual search for images of dark doors starting with a bright-door example

Full size image
Fig. 7.15
figure 15

A relevance feedback

Full size image
Fig. 7.16
figure 16

Lateral browsing for an image “from behind the pitcher in a baseball game…”

Full size image
Fig. 7.17
figure 17

Alternative ways to browse for images “from behind the pitcher …”

Full size image
Fig. 7.18
figure 18

Geo-temporal browsing in action

Full size image

Author information

Authors and Affiliations

  1. Knowledge Media Institute, The Open University, Milton Keynes, MK7 6AA, United Kingdom

    Stefan Rüger

Authors
  1. Stefan Rüger
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Stefan Rüger .

Editor information

Editors and Affiliations

  1. , Department of Information Engineering, University of Padua, Via G. Gradenigo, 6, Padua, 35131, Italy

    Massimo Melucci

  2. Yahoo! Research Barcelona, Ocata 1, Barcelona, 08003, Spain

    Ricardo Baeza-Yates

Rights and permissions

Reprints and permissions

Copyright information

© 2011 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Rüger, S. (2011). Multimedia Resource Discovery. In: Melucci, M., Baeza-Yates, R. (eds) Advanced Topics in Information Retrieval. The Information Retrieval Series, vol 33. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20946-8_7

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-20946-8_7

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-20945-1

  • Online ISBN: 978-3-642-20946-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation