Image annotation using multi-view non-negative matrix factorization with different number of basis vectors
| Autor: | Mansour Jamzad, Roya Rad |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Basis (linear algebra)
Standard test image business.industry 020206 networking & telecommunications Pattern recognition 02 engineering and technology Non-negative matrix factorization Matrix decomposition Set (abstract data type) ComputingMethodologies_PATTERNRECOGNITION Automatic image annotation Distance matrix Signal Processing 0202 electrical engineering electronic engineering information engineering Media Technology 020201 artificial intelligence & image processing Computer Vision and Pattern Recognition Artificial intelligence Electrical and Electronic Engineering business Image retrieval Mathematics |
| Zdroj: | Journal of Visual Communication and Image Representation. 46:1-12 |
| ISSN: | 1047-3203 |
| DOI: | 10.1016/j.jvcir.2017.03.005 |
| Popis: | Automatic Image Annotation (AIA) helps image retrieval systems by predicting tags for images. In this paper, we propose an AIA system using Non-negative Matrix Factorization (NMF) framework. The NMF framework discovers a latent space, by factorizing data into a set of non-negative basis and coefficients. To model the images, multiple features are extracted, each one represents images from a specific view. We use multi-view graph regularization NMF and allow NMF to choose a different number of basis vectors for each view. For tag prediction, each test image is mapped onto the multiple latent spaces. The distances of images in these spaces are used to form a unified distance matrix. The weights of distances are learned automatically. Then a search-based method is used to predict tags based on tags of nearest neighbors’. We evaluate our method on three datasets and show that it is competitive with the current state-of-the-art methods. |
| Databáze: | OpenAIRE |
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