Abstract
The huge collections of unconstrained videos have amplified the so-called semantic gap for content-based video retrieval. Therefore, new efficient approaches with higher generalisation power are needed. In this work, we present an interactive video retrieval approach based on latent topics to cope with the semantic gap in an efficient way. A supervised Symmetric extension of probabilistic Latent Semantic Analysis model is presented (sSpLSA). Then, this model is adapted to an on-line interactive information retrieval problem and it is applied to a video retrieval framework based on explicit short-term Relevance Feedback (RF) where queries are inside the database. Finally, several retrieval simulations using the Consumer Columbia Video (CCV) database are performed to compare the proposed approach with a distance-based RF baseline.
This work was partially supported by FPU-AP-2009-4435 from the Spanish Ministry of Education, PROMETEO/2010/028 project from Generalitat Valenciana and P1-1B2010-27 project from the Plan de Promoció de la Investigació UJI.
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Fernández-Beltran, R., Pla, F. (2013). An Interactive Video Retrieval Approach Based on Latent Topics. In: Petrosino, A. (eds) Image Analysis and Processing – ICIAP 2013. ICIAP 2013. Lecture Notes in Computer Science, vol 8156. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41181-6_30
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DOI: https://doi.org/10.1007/978-3-642-41181-6_30
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