31 search results - page 2 / 7 » Learning Linear Discriminant Projections for Dimensionality ... |
ICCV
2007
IEEE
14 years 6 months ago
2007
IEEE
In this paper we propose to transform an image descriptor so that nearest neighbor (NN) search for correspondences becomes the optimal matching strategy under the assumption that ...
CIVR
2008
Springer
13 years 6 months ago
2008
Springer
Since the emergence of extensive multimedia data, feature fusion has been more and more important for image and video retrieval, indexing and annotation. Existing feature fusion t...
NIPS
2008
13 years 6 months ago
2008
Probabilistic topic models have become popular as methods for dimensionality reduction in collections of text documents or images. These models are usually treated as generative m...
CVPR
2007
IEEE
14 years 6 months ago
2007
IEEE
Dimensionality reduction is an important issue when facing high-dimensional data. For supervised dimensionality reduction, Linear Discriminant Analysis (LDA) is one of the most po...
PSIVT
2009
Springer
13 years 11 months ago
2009
Springer
PCA-SIFT is an extension to SIFT which aims to reduce SIFT’s high dimensionality (128 dimensions) by applying PCA to the gradient image patches. However PCA is not a discriminati...