Saliency Detection for Hyperspectral Images via Sparse-Non Negative-Matrix-Factorization and novel Distance Measures

Autor:	Cristiano Tamborrino, Rosa Maria Mininni, Antonella Falini, Graziano Castellano, Francesca Mazzia, Annalisa Appice, Donato Malerba
Rok vydání:	2020
Předmět:	Pixel Computer science business.industry Dimensionality reduction Multispectral image ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Hyperspectral imaging Pattern recognition Distance measures Non-negative matrix factorization Image (mathematics) Salient Artificial intelligence business
Zdroj:	EAIS
DOI:	10.1109/eais48028.2020.9122749
Popis:	Saliency detection is a very active area in computer vision. When hyperspectral images are analyzed, a big amount of data need to be processed. Hence, dimensionality reduction techniques are used to highlight salient pixels allowing us to neglect redundant features. We propose a bottom-up approach based on two main ingredients: sparse non negative matrix factorization (SNMF) and spatial and spectral distances between the input image and the reconstructed one. In particular, we use both well known and novel distance functions. The method is validated on both hyperspectral and multispectral images.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::c49a32609d584956844551a69d68e920 https://doi.org/10.1109/eais48028.2020.9122749 Zobrazit plný text záznamu