Placing images with refined language models and similarity search with PCA-reduced VGG features

G. Kordopatis-Zilos; A. Popescu; S. Papadopoulos; Y. Kompatsiaris

Conference papers

Placing images with refined language models and similarity search with PCA-reduced VGG features

G. Kordopatis-Zilos ¹ A. Popescu ² S. Papadopoulos ¹ Y. Kompatsiaris ¹

1 CERTH ITI - CERTH Information Technologies Institute

2 DIASI - Département Intelligence Ambiante et Systèmes Interactifs

Abstract : We describe the participation of the CERTH/CEA-LIST team in the MediaEval 2016 Placing Task. We submitted five runs to the estimation-based sub-task: one based only on text by employing a Language Model-based approach with several refinements, one based on visual content, using geospatial clustering over the most visually similar images, and three based on a hybrid scheme exploiting both visual and textual cues from the multimedia items, trained on datasets of different size and origin. The best results were obtained by a hybrid approach trained with external training data and using two publicly available gazetteers.

Keywords : Similarity search Visual content Visual languages Multimedia items Language model Hybrid scheme Hybrid approach Geospatial clustering Different sizes Computational linguistics

Document type :

Conference papers

Domain :

Computer Science [cs]

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https://hal-cea.archives-ouvertes.fr/cea-01843183
Contributor : Léna Le Roy Connect in order to contact the contributor
Submitted on : Wednesday, July 18, 2018 - 3:55:57 PM
Last modification on : Thursday, February 17, 2022 - 10:08:05 AM

Placing images with refined language models and similarity search with PCA-reduced VGG features

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Placing images with refined language models and similarity search with PCA-reduced VGG features

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