From pixels to sentiment: fine-tuning CNNs for visual sentiment prediction
Visualitza/Obre
10.1016/j.imavis.2017.01.011
Inclou dades d'ús des de 2022
Cita com:
hdl:2117/102593
Tipus de documentArticle
Data publicació2017-02-05
Condicions d'accésAccés obert
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continguts d'aquesta obra estan subjectes a la llicència de Creative Commons
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Reconeixement-NoComercial-SenseObraDerivada 3.0 Espanya
ProjectePROCESADO DE INFORMACION HETEROGENEA Y SEÑALES EN GRAFOS PARA BIG DATA. APLICACION EN CRIBADO DE ALTO RENDIMIENTO, TELEDETECCION, MULTIMEDIA Y HCI. (MINECO-TEC2013-43935-R)
COMPUTACION DE ALTAS PRESTACIONES VII (MINECO-TIN2015-65316-P)
BARCELONA SUPERCOMPUTING CENTER - CENTRO. NACIONAL DE SUPERCOMPUTACION (MINECO-SEV-2015-0493)
COMPUTACION DE ALTAS PRESTACIONES VII (MINECO-TIN2015-65316-P)
BARCELONA SUPERCOMPUTING CENTER - CENTRO. NACIONAL DE SUPERCOMPUTACION (MINECO-SEV-2015-0493)
Abstract
Visual multimedia have become an inseparable part of our digital social lives, and they often capture moments tied with deep affections. Automated visual sentiment analysis tools can provide a means of extracting the rich feelings and latent dispositions embedded in these media. In this work, we explore how Convolutional Neural Networks (CNNs), a now de facto computational machine learning tool particularly in the area of Computer Vision, can be specifically applied to the task of visual sentiment prediction. We accomplish this through fine-tuning experiments using a state-of-the-art CNN and via rigorous architecture analysis, we present several modifications that lead to accuracy improvements over prior art on a dataset of images from a popular social media platform. We additionally present visualizations of local patterns that the network learned to associate with image sentiment for insight into how visual positivity (or negativity) is perceived by the model.
CitacióCampos, V., Jou, B., Giro, X. From pixels to sentiment: fine-tuning CNNs for visual sentiment prediction. "Image and vision computing", 5 Febrer 2017, vol. 65, p. 15-22.
ISSN0262-8856
Versió de l'editorhttp://www.sciencedirect.com/science/article/pii/S0262885617300355
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campos-2017-imavis.pdf | Postprint | 2,053Mb | Visualitza/Obre |