Recognizing Thousands of Legal Entities through Instance-based Visual Classification
| Autor: | Pierre Letessier, Olivier Buisson, Alexis Joly, Patrick Valduriez, Valentin Leveau |
|---|---|
| Přispěvatelé: | Scientific Data Management (ZENITH), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Institut National de l'Audiovisuel (INA), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Inria Sophia Antipolis - Méditerranée (CRISAM) |
| Rok vydání: | 2014 |
| Předmět: |
World Wide Web
Computer science 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing 02 engineering and technology 010501 environmental sciences Corporate identity Web crawler [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing 01 natural sciences 0105 earth and related environmental sciences |
| Zdroj: | ACM Multimedia 22nd International Conference on Multimedia MM: Conference on Multimedia MM: Conference on Multimedia, Nov 2014, Orlando, FL, United States. pp.1029-1032, ⟨10.1145/2647868.2655038⟩ |
| Popis: | International audience; This paper considers the problem of recognizing legal en-tities in visual contents in a similar way to named-entity recognizers for text documents. Whereas previous works were restricted to the recognition of a few tens of logotypes, we generalize the problem to the recognition of thousands of legal persons, each being modeled by a rich corporate identity automatically built from web images. We intro-duce a new geometrically-consistent instance-based classifi-cation method that is shown to outperform state-of-the-art techniques on several challenging datasets while being much more scalable. Further experiments performed on an au-tomatic web crawl of 5,824 legal entities demonstrates the scalability of the approach. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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