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In particular, this kind of distribution is present in content-based video retrieval systems, image classification based on local descriptors, rotation-invariant shape matching, and others.
Although these approaches are not more popular than those based on local descriptors, this method category is also important to consider.
Face recognition based on local descriptors has been recently recognized as the state-of-the-art design framework for problems of facial identification and verification.
The chosen solution based on local descriptors is later evaluated in a cross-database scenario with the three mentioned datasets, and full dataset 5-fold cross validation.
Given the diversity of the existing approaches, the main objective of this paper is to present a comprehensive, in-depth comparative analysis of the recent face recognition methodologies based on local descriptors.
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Although most quantization-based global representations are based on local image descriptors (such as SIFT [2] and SURF [12]), a few important spatial relevant information of these local features are generated by the local feature detectors [13], such as coordinates, scale, orientation, or saliency of local feature points apart from the local feature descriptors.
Leibe et al. proposed an implicit object model based on local invariant descriptors that jointly learns the discriminant descriptors for an object and their spatial relationships [31].
In most CBIR systems, global image representations (such as BoW [3], VLAD [8], and FV [10]) are based on local image descriptors, whereas the output of the feature detector information is ignored.
Most image registration methods are based on local feature descriptor matching techniques.
A novel set of 3D descriptors, based on local grid-based COSMO σ-profiles (LSPs), has been recently proposed as a promising alternative to force-field based MIFs in 3D-QSAR [1].
Novel molecular descriptors based on local spectral moments of the bond matrix are defined.
More suggestions(15)
based on local resources
based on several descriptors
based on local traditions
based on local data
based on local conditions
based on local Idioms
based on structural descriptors
based on local issues
based on local needs
based on local recommendations
based on physiographic descriptors
based on vectorial descriptors
based on continuous descriptors
based on local guidelines
based on visual descriptors
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