In summary,the multi feature crowd fusion algorithm is shown in Fig. 2.
To this end, first a novel spatial-temporal multi-feature is proposed by joining super-pixel based multi-appearance features and multi-motion features to solve fast moving crowd counting.
The proposed approach utilizes support vector regression and spatial-temporal multi-features to boost deep cumulative attribute learning.
A novel face recognition method based on multi-features using a neural networks committee (NNC) machine is proposed in this paper.
However, the extracted original features still with high dimensional and include superfluous information, the multi-features fusion technique PCA is used to merge the original features and reduce the dimension, the typical sensitive features are extracted.
Third, a novel boosting deep attribute Learning algorithm is proposed based on late fusion of proposed deep cumulative attribute learning and proposed spatial-temporal multi-features based support vector regression for improving predication performance of deep learning.
This framework is based on a novel multi-feature and adaptive spectral segmentation (MFASS) algorithm.
This paper describes the development of a new thermo-invariant material standard (new multi-feature bar (MFB) standard) and an associated procedure for the identification of 21 geometric errors on 3-axis machine tools (MT).
multi-features neural network classifier.
