MACNet: Multi-scale atrous convolution networks for food places classification in egocentric photo-streams
| Autor: | Sarker MMK, Rashwan HA, Talavera E, Banu SF, Radeva P, Puig D |
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| Přispěvatelé: | Universitat Rovira i Virgili |
| Rok vydání: | 2019 |
| Předmět: |
ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
Deep learning Interdisciplinar Engenharias iv Arquitetura urbanismo e design Administração pública e de empresas ciências contábeis e turismo Food pattern classification Educação Visual lifelogging Linguística e literatura Ciências ambientais Egocentric photo-streams Geociências Ciência da computação |
| Zdroj: | 14th Conference On Artificial Intelligence In Medicine, Aime 2013 14th Conference On Artificial Intelligence In Medicine, Aime 2013. 11133 LNCS 423-433 Repositori Institucional de la Universitat Rovira i Virgili Universitat Rovira i virgili (URV) |
| DOI: | 10.1007/978-3-030-11021-5_26 |
| Popis: | © Springer Nature Switzerland AG 2019. First-person (wearable) camera continually captures unscripted interactions of the camera user with objects, people, and scenes reflecting his personal and relational tendencies. One of the preferences of people is their interaction with food events. The regulation of food intake and its duration has a great importance to protect against diseases. Consequently, this work aims to develop a smart model that is able to determine the recurrences of a person on food places during a day. This model is based on a deep end-to-end model for automatic food places recognition by analyzing egocentric photo-streams. In this paper, we apply multi-scale Atrous convolution networks to extract the key features related to food places of the input images. The proposed model is evaluated on an in-house private dataset called “EgoFoodPlaces”. Experimental results shows promising results of food places classification in egocentric photo-streams. |
| Databáze: | OpenAIRE |
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