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2014 | OriginalPaper | Chapter

Representativeness and Diversity in Photos via Crowd-Sourced Media Analysis

Authors : Anca-Livia Radu, Julian Stöttinger, Bogdan Ionescu, María Menéndez, Fausto Giunchiglia

Published in: Adaptive Multimedia Retrieval: Semantics, Context, and Adaptation

Publisher: Springer International Publishing

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Abstract

In this paper we address the problem of user-adapted image retrieval. First, we provide a survey of the performance of the existing social media retrieval platforms and highlight their limitations. In this context, we propose a hybrid, two step, machine and human automated media analysis approach. It aims to improve retrieval relevance by selecting a small number of representative and diverse images from a noisy set of candidate images (e.g. the case of Internet media). In the machine analysis step, to ensure representativeness, images are re-ranked according to the similarity to the “most common” image in the set. Further, to ensure also the diversity of the results, images are clustered and the best ranked images among the most representative in each cluster are retained. The human analysis step aims to bridge further inherent descriptor semantic gap. The retained images are further refined via crowd-sourcing which adapts the results to human. The method was validated in the context of the retrieval of images with monuments using a data set of more than 25.000 images retrieved from various social image search platforms.
Literature
1.
go back to reference Bartolini, I., Ciaccia, P.: Multi-dimensional keyword-based image annotation and search. In: ACM International Workshop on Keyword Search on Structured Data, New York, USA, pp. 1–6 (2010) Bartolini, I., Ciaccia, P.: Multi-dimensional keyword-based image annotation and search. In: ACM International Workshop on Keyword Search on Structured Data, New York, USA, pp. 1–6 (2010)
2.
go back to reference Kennedy, L.S., Naaman, M.: Generating diverse and representative image search results for landmarks. In: International Conference on World Wide Web, New York, NY, USA, pp. 297–306 (2008) Kennedy, L.S., Naaman, M.: Generating diverse and representative image search results for landmarks. In: International Conference on World Wide Web, New York, NY, USA, pp. 297–306 (2008)
3.
go back to reference Popescu, A., Moëllic, P.A., Kanellos, I., Landais, R.: Lightweight web image reranking. In: ACM International Conference on Multimedia, New York, NY, USA, pp. 657–660 (2009) Popescu, A., Moëllic, P.A., Kanellos, I., Landais, R.: Lightweight web image reranking. In: ACM International Conference on Multimedia, New York, NY, USA, pp. 657–660 (2009)
4.
go back to reference Fergus, R., Perona, P., Zisserman, A.: A visual category filter for Google images. In: Pajdla, T., Matas, J.G. (eds.) ECCV 2004. LNCS, vol. 3021, pp. 242–256. Springer, Heidelberg (2004) CrossRef Fergus, R., Perona, P., Zisserman, A.: A visual category filter for Google images. In: Pajdla, T., Matas, J.G. (eds.) ECCV 2004. LNCS, vol. 3021, pp. 242–256. Springer, Heidelberg (2004) CrossRef
5.
go back to reference Nguyen, N.V., Ogier, J.M., Tabbone, S., Boucher, A.: Text retrieval relevance feedback techniques for bag of words model in CBIR. In: International Conference on Machine Learning and Pattern Recognition (2009) Nguyen, N.V., Ogier, J.M., Tabbone, S., Boucher, A.: Text retrieval relevance feedback techniques for bag of words model in CBIR. In: International Conference on Machine Learning and Pattern Recognition (2009)
6.
go back to reference Larson, M., Rae, A., Demarty, C.H., Kofler, C., Metze, F., Troncy, R., Mezaris, V., Jones, G.J. In: MediaEval 2011 Workshop at Interspeech 2011, vol. 807, CEUR-WS.org, 1–2 September 2011 Larson, M., Rae, A., Demarty, C.H., Kofler, C., Metze, F., Troncy, R., Mezaris, V., Jones, G.J. In: MediaEval 2011 Workshop at Interspeech 2011, vol. 807, CEUR-WS.org, 1–2 September 2011
7.
go back to reference Crandall, D.J., Backstrom, L., Huttenlocher, D., Kleinberg, J.: Mapping the world’s photos. In: International Conference on World Wide Web, New York, NY, USA, pp. 761–770 (2009) Crandall, D.J., Backstrom, L., Huttenlocher, D., Kleinberg, J.: Mapping the world’s photos. In: International Conference on World Wide Web, New York, NY, USA, pp. 761–770 (2009)
8.
go back to reference Hays, J., Efros, A.A.: Im2gps: estimating geographic information from a single image. In: IEEE International Conference on Computer Vision and Pattern Recognition (2008) Hays, J., Efros, A.A.: Im2gps: estimating geographic information from a single image. In: IEEE International Conference on Computer Vision and Pattern Recognition (2008)
9.
go back to reference Van de Weijer, J., Schmid, C., Verbeek, J., Larlus, D.: Learning color names for real-world applications. IEEE Trans. Image Process. 18(7), 1512–1523 (2009) MathSciNetCrossRef Van de Weijer, J., Schmid, C., Verbeek, J., Larlus, D.: Learning color names for real-world applications. IEEE Trans. Image Process. 18(7), 1512–1523 (2009) MathSciNetCrossRef
Metadata
Title
Representativeness and Diversity in Photos via Crowd-Sourced Media Analysis
Authors
Anca-Livia Radu
Julian Stöttinger
Bogdan Ionescu
María Menéndez
Fausto Giunchiglia
Copyright Year
2014
DOI
https://doi.org/10.1007/978-3-319-12093-5_6