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High-level spatial relation and configuration modeling issues are gaining momentum in the image analysis and pattern recognition fields.
Multi-label classification problems usually occur in tasks related to information retrieval, like text and image annotation, and are receiving increasing attention from the machine learning and pattern recognition fields.
LBP and some variants of it can achieve impressive accuracy in pattern recognition fields with a strong texture discrimination capability.
In literature, there are many studies that focus on speech recognition and speaker recognition fields in stressful talking environments [5 13].
The algorithms of major CAD systems are developed by using techniques and theories of the pattern recognition field, and thus the CAD is involved as one of the pattern recognition fields [6].
These coefficients have been used in the stressful speech and speaker recognition fields because such coefficients outperform other features in the two fields and because they provide a high-level approximation of human auditory perception [25 27].
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Facial expression recognition is an important research issue in the pattern recognition field.
The classification of large number of object categories is a challenging trend in the pattern recognition field.
Numerous dimensionality reduction methods have achieved impressive performance in face recognition field due to their potential to exploit the intrinsic structure of images and to enhance the computational efficiency.
In the speech recognition field, GSS-based dereverberation using clean speech models showed a significant improvement [22].
The use of the within-class covariance (WCC) matrix to normalize data variances has become widely dispread in the speaker recognition field [41, 43].
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