Fuzzy clustering to encode contextual information in artistic image classification
Fecha
2022Versión
Acceso abierto / Sarbide irekia
Tipo
Contribución a congreso / Biltzarrerako ekarpena
Versión
Versión aceptada / Onetsi den bertsioa
Identificador del proyecto
Impacto
|
10.1007/978-3-031-08974-9_28
Resumen
Automatic art analysis comprises of utilizing diverse processing methods to classify and categorize works of art. When working with this kind of pictures, we have to take under consideration different considerations compared to classical picture handling, since works of art alter definitely depending on the creator, the scene delineated or their aesthetic fashion. This extra data improves the vis ...
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Automatic art analysis comprises of utilizing diverse processing methods to classify and categorize works of art. When working with this kind of pictures, we have to take under consideration different considerations compared to classical picture handling, since works of art alter definitely depending on the creator, the scene delineated or their aesthetic fashion. This extra data improves the visual signals gotten from the images and can lead to better performance. However, this information needs to be modeled and embed alongside the visual features of the image. This is often performed utilizing deep learning models, but they are expensive to train. In this paper we utilize the Fuzzy C-Means algorithm to create a embedding strategy based on fuzzy memberships to extract relevant information from the clusters present in the contextual information. We extend an existing state-of-the-art art classification system utilizing this strategy to get a new version that presents similar results without training additional deep learning models. [--]
Materias
Clustering,
Fuzzy C Means,
Image classification,
Representation learning,
Clustering
Editor
Springer
Publicado en
Ciucci, D.; Couso, I.; Medina, J.; Slezak, D.; Petturiti, D.; Bouchon-Meunier, B.; Jager, R. R. (Eds.). Information Processing and Management of Uncertainty in Knowledge-Based Systems, IPMU 2022. Cham: Springer International Publishing; 2022. p.355-366 978-3-031-08973-2
Departamento
Universidad Pública de Navarra. Departamento de Estadística, Informática y Matemáticas /
Nafarroako Unibertsitate Publikoa. Estatistika, Informatika eta Matematika Saila
Versión del editor
Entidades Financiadoras
Javier Fumanal Idocin and Humberto Bustince’s research has been supported by the project PID2019-108392GB I00 (AEI/10.13039/501100011033). Zdenko Takác and Lúbomíra Horanská’s research has been supported by the grant VEGA 1/0267/21.