publication . Other literature type . Doctoral thesis . 2013

Advancing the Relevance Criteria for Video Search and Visual Summarization

Rudinac, S.;
Open Access
  • Published: 06 May 2013
  • Publisher: Delft University of Technology
  • Country: Netherlands
Abstract
To facilitate finding of relevant information in ever-growing multimedia collections, a number of multimedia information retrieval solutions have been proposed over the past years. The essential element of any such solution is the relevance criterion deployed to select or rank the items from a multimedia collection to be presented to the user. Due to the inability of computational approaches to interpret multimedia items and their semantic relations in the same way as humans, the research community has mainly focused on the relevance criteria that can be handled by the modern computers, e.g., finding images or videos depicting a particular object, setting or eve...
Subjects
free text keywords: multimedia information retrieval, video search, visual summarization, query performance prediction, multimedia content analysis, multimodal fusion, social media, machine learning, image set evaluation, image aesthetic appeal, sentiment analysis, crowdsourcing
Funded by
EC| PETAMEDIA
Project
PETAMEDIA
PEer-to-peer TAgged MEDIA
  • Funder: European Commission (EC)
  • Project Code: 216444
  • Funding stream: FP7 | SP1 | ICT
Download fromView all 3 versions
TU Delft Repository
Doctoral thesis . 2013
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Doctoral thesis . 2013
Provider: NARCIS
http://dx.doi.org/10.4233/uuid...
Other literature type . 2013
Provider: Datacite
107 references, page 1 of 8

[11] O. Chapelle and S. S. Keerthi, “Efficient algorithms for ranking with svms,” Inf. Retr., vol. 13, no. 3, pp. 201-215, jun 2010.

[12] M. Clements, “Personalised access to social media,” Ph.D. dissertation, TU Delft, Delft, The Netherlands, 2010.

[13] M. Clements, A. P. De Vries, and M. J. T. Reinders, “The task-dependent effect of tags and ratings on social media access,” ACM Trans. Inf. Syst., vol. 28, pp. 21:1-21:42, November 2010.

[14] J. Coutaz, “Multimedia and multimodal user interfaces: A taxonomy for software engineering research issues,” in East-West HCI'92, August 1992, pp. 229-239.

[15] S. Cronen-Townsend, Y. Zhou, and W. B. Croft, “Predicting query performance,” in Proc. 25th annual int. ACM SIGIR conf. on Research and development in information retrieval, ser. SIGIR '02. ACM, 2002, pp. 299-306.

[16] H. T. Dang, “Overview of DUC 2006,” in Proceedings of the Document Understanding Conference, ser. DUC '06, 2006.

[17] R. Datta, D. Joshi, J. Li, and J. Z. Wang, “Studying aesthetics in photographic images using a computational approach,” in Proceedings of the 9th European conference on Computer Vision - Volume Part III, ser. ECCV'06. Berlin, Heidelberg: Springer-Verlag, 2006, pp. 288-301.

[18] K. Denecke, “Using sentiwordnet for multilingual sentiment analysis,” in Data Engineering Workshop, 2008. ICDEW 2008. IEEE 24th International Conference on, april 2008, pp. 507 -512. [OpenAIRE]

[19] C. Dwork, R. Kumar, M. Naor, and D. Sivakumar, “Rank aggregation methods for the web,” in Proceedings of the 10th international conference on World Wide Web, ser. WWW '01. New York, NY, USA: ACM, 2001, pp. 613-622.

[20] C. Eickhoff and A. de Vries, “How crowdsourcable is your task?” ser. CSDM '11, 2011.

[21] A. Esuli and F. Sebastiani, “SentiWordNet: A publicly available lexical resource for opinion mining,” in Proceedings of the 5th Conference on Language Resources and Evaluation, ser. LREC '06, 2006, pp. 417-422. [OpenAIRE]

[22] B. J. Frey and D. Dueck, “Clustering by passing messages between data points,” Science, vol. 315, pp. 972-976, 2007.

[23] A. Hanjalic and L.-Q. Xu, “Affective video content representation and modeling,” Multimedia, IEEE Transactions on, vol. 7, no. 1, pp. 143 - 154, feb. 2005. [OpenAIRE]

[24] Q. Hao, R. Cai, X.-J. Wang, J.-M. Yang, Y. Pang, and L. Zhang, “Generating location overviews with images and tags by mining user-generated travelogues,” in Proceedings of the 17th ACM international conference on Multimedia, ser. MM '09. New York, NY, USA: ACM, 2009, pp. 801-804.

[25] D. Harman and P. Over, “The DUC summarization evaluations,” in Proceedings of the second international conference on Human Language Technology Research, ser. HLT '02. Morgan Kaufmann Publishers Inc., 2002, pp. 44-51.

107 references, page 1 of 8
Abstract
To facilitate finding of relevant information in ever-growing multimedia collections, a number of multimedia information retrieval solutions have been proposed over the past years. The essential element of any such solution is the relevance criterion deployed to select or rank the items from a multimedia collection to be presented to the user. Due to the inability of computational approaches to interpret multimedia items and their semantic relations in the same way as humans, the research community has mainly focused on the relevance criteria that can be handled by the modern computers, e.g., finding images or videos depicting a particular object, setting or eve...
Subjects
free text keywords: multimedia information retrieval, video search, visual summarization, query performance prediction, multimedia content analysis, multimodal fusion, social media, machine learning, image set evaluation, image aesthetic appeal, sentiment analysis, crowdsourcing
Funded by
EC| PETAMEDIA
Project
PETAMEDIA
PEer-to-peer TAgged MEDIA
  • Funder: European Commission (EC)
  • Project Code: 216444
  • Funding stream: FP7 | SP1 | ICT
Download fromView all 3 versions
TU Delft Repository
Doctoral thesis . 2013
Provider: NARCIS
NARCIS
Doctoral thesis . 2013
Provider: NARCIS
http://dx.doi.org/10.4233/uuid...
Other literature type . 2013
Provider: Datacite
107 references, page 1 of 8

[11] O. Chapelle and S. S. Keerthi, “Efficient algorithms for ranking with svms,” Inf. Retr., vol. 13, no. 3, pp. 201-215, jun 2010.

[12] M. Clements, “Personalised access to social media,” Ph.D. dissertation, TU Delft, Delft, The Netherlands, 2010.

[13] M. Clements, A. P. De Vries, and M. J. T. Reinders, “The task-dependent effect of tags and ratings on social media access,” ACM Trans. Inf. Syst., vol. 28, pp. 21:1-21:42, November 2010.

[14] J. Coutaz, “Multimedia and multimodal user interfaces: A taxonomy for software engineering research issues,” in East-West HCI'92, August 1992, pp. 229-239.

[15] S. Cronen-Townsend, Y. Zhou, and W. B. Croft, “Predicting query performance,” in Proc. 25th annual int. ACM SIGIR conf. on Research and development in information retrieval, ser. SIGIR '02. ACM, 2002, pp. 299-306.

[16] H. T. Dang, “Overview of DUC 2006,” in Proceedings of the Document Understanding Conference, ser. DUC '06, 2006.

[17] R. Datta, D. Joshi, J. Li, and J. Z. Wang, “Studying aesthetics in photographic images using a computational approach,” in Proceedings of the 9th European conference on Computer Vision - Volume Part III, ser. ECCV'06. Berlin, Heidelberg: Springer-Verlag, 2006, pp. 288-301.

[18] K. Denecke, “Using sentiwordnet for multilingual sentiment analysis,” in Data Engineering Workshop, 2008. ICDEW 2008. IEEE 24th International Conference on, april 2008, pp. 507 -512. [OpenAIRE]

[19] C. Dwork, R. Kumar, M. Naor, and D. Sivakumar, “Rank aggregation methods for the web,” in Proceedings of the 10th international conference on World Wide Web, ser. WWW '01. New York, NY, USA: ACM, 2001, pp. 613-622.

[20] C. Eickhoff and A. de Vries, “How crowdsourcable is your task?” ser. CSDM '11, 2011.

[21] A. Esuli and F. Sebastiani, “SentiWordNet: A publicly available lexical resource for opinion mining,” in Proceedings of the 5th Conference on Language Resources and Evaluation, ser. LREC '06, 2006, pp. 417-422. [OpenAIRE]

[22] B. J. Frey and D. Dueck, “Clustering by passing messages between data points,” Science, vol. 315, pp. 972-976, 2007.

[23] A. Hanjalic and L.-Q. Xu, “Affective video content representation and modeling,” Multimedia, IEEE Transactions on, vol. 7, no. 1, pp. 143 - 154, feb. 2005. [OpenAIRE]

[24] Q. Hao, R. Cai, X.-J. Wang, J.-M. Yang, Y. Pang, and L. Zhang, “Generating location overviews with images and tags by mining user-generated travelogues,” in Proceedings of the 17th ACM international conference on Multimedia, ser. MM '09. New York, NY, USA: ACM, 2009, pp. 801-804.

[25] D. Harman and P. Over, “The DUC summarization evaluations,” in Proceedings of the second international conference on Human Language Technology Research, ser. HLT '02. Morgan Kaufmann Publishers Inc., 2002, pp. 44-51.

107 references, page 1 of 8
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