Classifiers Combination Techniques: A Comprehensive Review

Mohandes, M.; Deriche, Mohamed; Aliyu, Salihu O.

doi:10.1109/access.2018.2813079

Cited by 109 publications

(72 citation statements)

References 67 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The features extracted from the dual-domain are fused to form a multi-dimensional feature vector [ 41 , 42 , 43 , 44 , 45 , 46 ]. After feature extraction, the quality regression from feature space to image quality is conducted, which can be denoted as

where

is a quality regression function achieved by feature pooling strategy,

represents the extracted feature vector, and Q is the quality of tested image.…”

Section: Proposed Dff-iqa Methodsmentioning

confidence: 99%

No-Reference Image Quality Assessment Based on Dual-Domain Feature Fusion

Cui

2020

Entropy

View full text Add to dashboard Cite

Image quality assessment (IQA) aims to devise computational models to evaluate image quality in a perceptually consistent manner. In this paper, a novel no-reference image quality assessment model based on dual-domain feature fusion is proposed, dubbed as DFF-IQA. Firstly, in the spatial domain, several features about weighted local binary pattern, naturalness and spatial entropy are extracted, where the naturalness features are represented by fitting parameters of the generalized Gaussian distribution. Secondly, in the frequency domain, the features of spectral entropy, oriented energy distribution, and fitting parameters of asymmetrical generalized Gaussian distribution are extracted. Thirdly, the features extracted in the dual-domain are fused to form the quality-aware feature vector. Finally, quality regression process by random forest is conducted to build the relationship between image features and quality score, yielding a measure of image quality. The resulting algorithm is tested on the LIVE database and compared with competing IQA models. Experimental results on the LIVE database indicate that the proposed DFF-IQA method is more consistent with the human visual system than other competing IQA methods.

show abstract

where

is a quality regression function achieved by feature pooling strategy,

represents the extracted feature vector, and Q is the quality of tested image.…”

Section: Proposed Dff-iqa Methodsmentioning

confidence: 99%

No-Reference Image Quality Assessment Based on Dual-Domain Feature Fusion

Cui

2020

Entropy

View full text Add to dashboard Cite

show abstract

“…The two methods differ in the way the classifier combination is performed. The first method, HGEI-i, performs the early fusion at the feature level, while HGEI-f performs the late fusion at the decision level [23].…”

Section: Proposed Recognition Methodsmentioning

confidence: 99%

Extending Appearance Based Gait Recognition with Depth Data

et al. 2019

View full text Add to dashboard Cite

Each individual describes unique patterns during their gait cycles. This information can be extracted from the live video stream and used for subject identification. In appearance based recognition methods, this is done by tracking silhouettes of persons across gait cycles. In recent years, there has been a profusion of sensors that in addition to RGB video images also provide depth data in real-time. When such sensors are used for gait recognition, existing RGB appearance based methods can be extended to get a substantial gain in recognition accuracy. In this paper, this is accomplished using information fusion techniques that combine features from extracted silhouettes, used in traditional appearance based methods, and the height feature that can now be estimated using depth data. The latter is estimated during the silhouette extraction step with minimal additional computational cost. Two approaches are proposed that can be implemented easily as an extension to existing appearance based methods. An extensive experimental evaluation was performed to provide insights into how much the recognition accuracy can be improved. The results are presented and discussed considering different types of subjects and populations of different height distributions.

show abstract

“…According to the study of Mohandes (Mohandes et al, 2018), fusion can be conducted in the sensor level, the feature level or the decision level, depending on the stage at which the fusion method operates. Here, we focus on the problem of classifiers fusion methods in the decision level according to the outputs of the classifiers, the fusion rules and the ensemble creation methods.…”

Section: Review Of Machine Learning Methodsmentioning

confidence: 99%

Shape recognition through multi-level fusion of features and classifiers

et al. 2019

View full text Add to dashboard Cite

Shape recognition is a fundamental problem and a special type of image classification, where each shape is considered as a class. Current approaches to shape recognition mainly focus on designing low-level shape descriptors, and classify them using some machine learning approaches. In order to achieve effective learning of shape features, it is essential to ensure that a comprehensive set of high quality features can be extracted from the original shape data. Thus we have been motivated to develop methods of fusion of features and classifiers for advancing the classification performance. In this paper, we propose a multi-level framework for fusion of features and classifiers in the setting of granular computing. The proposed framework involves creation of diversity among classifiers, through adopting feature selection and fusion to create diverse feature sets and to train diverse classifiers using different learn

show abstract

Classifiers Combination Techniques: A Comprehensive Review

Cited by 109 publications

References 67 publications

No-Reference Image Quality Assessment Based on Dual-Domain Feature Fusion

No-Reference Image Quality Assessment Based on Dual-Domain Feature Fusion

Extending Appearance Based Gait Recognition with Depth Data

Shape recognition through multi-level fusion of features and classifiers

Contact Info

Product

Resources

About