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Exact(15)
Extracted multiple features are utilized within a discriminative learning framework for recognizing interactions between people.
In our proposed framework, multiple features are extracted to represent 3D neuron data.
To achieve accurate detection, multiple features are typically combined for computing a saliency map, and a dense-sampling approach that examines numerous regions is widely used (both processes are computationally demanding).
Multiple features are widely used to characterize real-world datasets.
The multiple features are selected using the Sequential Forward Selection (SFS) algorithm.
The multiple features are selected using the sequential forward selection algorithm we called Co-occurrence of LBP (CoLBP).
Similar(45)
The integration of multiple features is important for action categorization and object recognition in videos, because single feature based representation hardly captures imaging variations and individual attributes.
This selectivity was more robust when larger numbers of surrounding items and multiple features were included in the display, and it was absent when only a few items were presented immediately outside the CRF.
A straightforward strategy to handle multiple features is to concatenate all the feature vectors into a single feature vector.
The fusion of multiple features is regarded as a positive step towards the development of extremely ambitious types of iris recognition [17].
By version 4, multiple features were added, such as editing photo labels, sorting photos, and adding photos as favorite, introducing a smell.
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