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Multi-Modal Correlated Centroid Space for Multi-Lingual Cross-Modal Retrieval




Published: 2015 April
Herausgeber: Hanbury, Allan and Kazai, Gabriella and Rauber, Andreas and Fuhr, Norbert
Buchtitel: Advances in Information Retrieval: 37th European Conference on IR Research (ECIR), Vienna, Austria.
Seiten: http://people.aifb.kit.edu/amo/ecir2015/
Verlag: Springer International Publishing
Erscheinungsort: Cham, Germany
Referierte Veröffentlichung
BibTeX

Kurzfassung
We present a novel cross-modal retrieval approach where the textual modality is present in different languages. We retrieve semantically similar documents across modalities in different languages using a correlated centroid space unsupervised retrieval (C2SUR) approach. C2SUR consists of two phases. In the first phase, we extract heterogeneous features from a multi-modal document and project it to a correlated space using kernel canonical correlation analysis (KCCA). In the second phase, correlated space centroids are obtained using clustering to retrieve cross-modal documents with different similarity measures. Experimental results show that C2SUR outperforms the existing state-of-the-art English cross-modal retrieval approaches and achieve similar results for other languages.

Weitere Informationen unter: Link
DOI Link: 10.1007/978-3-319-16354-3_9

Projekt

XLiMe



Forschungsgruppe

Web Science und Wissensmanagement


Forschungsgebiet
Maschinelles Lernen, Multimedia Annotation & Retrieval


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