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A multi-view-group non-negative matrix factorization approach for automatic image annotation

Rad, R ; Sharif University of Technology | 2018

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  1. Type of Document: Article
  2. DOI: 10.1007/s11042-017-5279-4
  3. Publisher: Springer New York LLC , 2018
  4. Abstract:
  5. In automatic image annotation (AIA) different features describe images from different aspects or views. Part of information embedded in some views is common for all views, while other parts are individual and specific. In this paper, we present the Mvg-NMF approach, a multi-view-group non-negative matrix factorization (NMF) method for an AIA system which considers both common and individual factors. The NMF framework discovers a latent space by decomposing data into a set of non-negative basis vectors and coefficients. The views divided into homogeneous groups and latent spaces are extracted for each group. After mapping the test images into these spaces, a unified distance matrix is computed from the distance between images in all spaces. Then a search-based method is used to propagate tags from the nearest neighbors to test images. The evaluation on three datasets commonly used for image annotation showed that the Mvg-NMF is highly competitive with the recent state-of-the-art works. © 2017, Springer Science+Business Media, LLC
  6. Keywords:
  7. AIA ; Automatic image annotation ; Multi-label classification ; Multi-view-group ; NMF ; Non negative matrix factorization ; Classification (of information) ; Factorization ; Image analysis ; Image annotation ; Image retrieval ; Vector spaces ; Automatic image annotations (AIA) ; Distance matrices ; Homogeneous group ; Individual factors ; Multi label classification ; Multi-views ; Nearest neighbors ; Nonnegative matrix factorization ; Matrix algebra
  8. Source: Multimedia Tools and Applications ; Volume 77, Issue 13 , 2018 , Pages 17109-17129 ; 13807501 (ISSN)
  9. URL: https://link.springer.com/article/10.1007/s11042-017-5279-4