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Image Retrieval in a Commercial Setting

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ImageCLEF

Part of the book series: The Information Retrieval Series ((INRE,volume 32))

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Abstract

This chapter provides an overview of image retrieval in a commercial setting. It details the types of resources available to commercial systems in conducting image retrieval research, and the challenges in using such resources. In particular the chapter discusses user generated content, click data, and how to evaluate commercial image search systems. It ends with a discussion of the role of benchmark efforts such as ImageCLEF in this type of research.

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References

  • Bishop C (1995) Neural networks for pattern recognition. Oxford University Press, Oxford

    Google Scholar 

  • Burges C, Shaked T, Renshaw E, Lazier A, Deeds M, Hamilton N, Hullender G (2005) Learning to rank using gradient descent. In: Proceedings of the 22nd International Conference on Machine Learning (ICML), pp 89–96

    Google Scholar 

  • Chapelle O, Haffner P, Vapnik V (1999) SVMs for histogram–based image classification. IEEE Transactions on Neural Networks 10(5)

    Google Scholar 

  • Chatzichristofis SA, Boutalis YS (2008) CEDD: Color and edge directivity descriptor: A compact descriptor for image indexing and retrieval. In: Proceedings of the 6th International Conference on Computer Vision Systems, pp 312–322

    Google Scholar 

  • Cheng E, Jing F, Zhang L, Jin H (2006) Scalable relevance feedback using click-through data for web image retrieval. In: Proceedings of the 14th annual ACM international conference on Multimedia. ACM press, pp 173–176

    Google Scholar 

  • Ciaramita M, Murdock V, Plachouras V (2008) Online learning from click data for sponsored search. In: Proceedings of the 17th International World Wide Web Conference, Beijing

    Google Scholar 

  • Duda R, Hart P, Stork D (2000) Pattern classification (2nd ed.) Wiley–Interscience

    Google Scholar 

  • Elsas J, Carvalho V, Carbonell J (2008) Fast learning of document ranking functions with the committee perceptron. In: Proceedings of the 1st ACM International Conference on Web Search and Data Mining. ACM press

    Google Scholar 

  • Harchaoui Z, Bach F (2007) Image classification with segmentation graph kernels. In: Proceedings of computer vision and pattern recognition

    Google Scholar 

  • Hauptmann A, Yan R, Lin WH (2007) How many high–level concepts will fill the semantic gap in news video retrieval? In: Proceedings of the 6th ACM international conference on Image and video retrieval. ACM press, pp 627–634

    Google Scholar 

  • Huang J, Kumar SR, Mitra M, Zhu WJ, Zabih R (1997) Image indexing using color correlograms. In: Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition. IEEE Computer Society, Washington, DC, USA, p 762

    Chapter  Google Scholar 

  • Joachims T (2002) Optimizing search engines using clickthrough data. In: Proceedings of the ACM Conference on Knowledge Discovery and Data Mining. ACM press

    Google Scholar 

  • Liu TY, Qin T, Xu J, Xiong W, Li H (2007) Letor: Benchmark dataset for research on learning to rank for information retrieval. In: SIGIR Workshop on Learning to Rank for Information Retrieval

    Google Scholar 

  • Rumelhart D, Hinton G, Williams R (1986) Learning internal representation by backpropagating errors. Nature 323(99):533–536

    Article  Google Scholar 

  • Salembier P, Sikora T (2002) Introduction to MPEG–7: Multimedia Content Description Interface. John Wiley & Sons, Inc., New York, NY, USA

    Google Scholar 

  • San Pedro J, Siersdorfer S (2009) Ranking and classifying attractiveness of photos in folksonomies. In: Proceedings of the WWW conference

    Google Scholar 

  • Snoek CGM, Huurnink B, Hollink L, de Rijke M, Schreiber G, Worring M (2007) Adding semantics to detectors for video retrieval. IEEE Transactions on Multimedia 9(5):975–986

    Article  Google Scholar 

  • Tamura H, Mori S, Yamawaki T (1978) Texture features corresponding to visual perception. IEEE Transactions on Systems, Man and Cybernetics 8(6)

    Google Scholar 

  • Tong H, He J, Li M, Ma WY, Zhang HJ, Zhang C (2006) Manifold–ranking–based keyword propagation for image retrieval. EURASIP Journal of Applied Signal Processing 2006(1):190–190

    Google Scholar 

  • Tong S, Chang E (2001) Support vector machine active learning for image retrieval. In: Proceedings of the 9th annual ACM international conference on Multimedia. ACM press

    Google Scholar 

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Correspondence to Vanessa Murdock .

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Murdock, V., van Zwol, R., Garcia, L., Olivares, X. (2010). Image Retrieval in a Commercial Setting. In: Müller, H., Clough, P., Deselaers, T., Caputo, B. (eds) ImageCLEF. The Information Retrieval Series, vol 32. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15181-1_26

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  • DOI: https://doi.org/10.1007/978-3-642-15181-1_26

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-15180-4

  • Online ISBN: 978-3-642-15181-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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