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Relative features were calculated to obtain further information.
We have presented the concept of relative features in [25].
Calculating relative features requires a relationship measurement, such as distance on similarity.
Almost all of them employed engineered low-level absolute and relative features for constructing MRF.
Several relative features were designed according to the methods of digital image progressing, pattern recognition and the theory of taxonomy.
The less prominent ones are detected by the second module by looking at relative features compared to the underlying surface colour.
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Fig. 8 Relative feature importance in Davis.
The relative SVM supervector method uses a relative feature approach.
Chen, et al [126], proposed an RUL prediction method based on relative feature and multivariate SVM (MSVM).
The experimental results show that the relative SSR subsystem outperforms the baseline and can obtain better performance with relative feature using a low-dimension SVM supervector.
Additionally, geometric methods exploit relative feature positions to estimate head pose; however, the accuracy is highly related to the feature-point extraction [30].
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