The current research on human re-id techniques mainly includes two aspects, i.e., feature extraction and distance metric learning.
There are mainly three subtopics on representation learning: feature selection, feature extraction, and distance metric learning [27].
After the extraction of the metric distribution under ℋ 1 and for a given threshold, the probability of correct detection PD can be calculated.
In Fig. 5, for each feature extraction method, each blur metric and each blur strength, the accuracies achieved with the optimal Θ for the specific blur strengths are plotted and compared with the classification rates achieved with traditional classification.
The Feature Extraction module represents the metric features of graspable objects such as their location, orientation, shape, and oriented surfaces.
In 3D key-frame extraction methods, the computational complexity metric is only used by Lee et al. in [22], where the computational complexity of Lee's and Huang's et al. [18] methods are compared.
The cell extraction well provided an integrated metric of source zone dissolution yielding a mean concentration of around 45% TCE solubility (taking into account dechlorination) that was equivalent to a DNAPL mass removal rate of 0.4 tonnes per annum over a 16 m2 cell cross sectional area of flow.
Arrays were scanned with Agilent Technologies Scanner (model G2505B) and numerical results were extracted with Feature Extraction version 9.5.3.1 using 018413_D_F_20071204 grid, GE1-v5_95_Feb07 protocol and GE1_QCM_Feb07 QC metric set.
Data were extracted using the Agilent Feature Extraction Software, v10.10.1.1, following the Agilent protocol ChIP_1010_Sep10, grid template 014741_D_F_20091202 and metric set ChIP_QCMT_Sep10.
The traditional approaches to this problem usually include two basic steps: feature extraction and the application of a distance metric, sometimes common space projection is also involved.
