Influence of kernel clustering on an RBFN

Zhu, Changming; Miao, Duoqian

doi:10.1049/trit.2019.0036

Cited by 31 publications

(21 citation statements)

References 22 publications

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“…The conventional FCM algorithm works on the local spatial information of pixels in images such that all neighboring regions of pixels cause high computation complexity due to analysis of spatial values at each iteration. Therefore, the proposed algorithm uses super pixel-based pre-segmentation [ 29 ] and density-based spatial clustering with noise (DBSCN) to decrease the computational complexity of Conventional FCM. Figure 3 presents the results of super pixel-based pre-segmentation.…”

Section: Overview Of Solution Frameworkmentioning

confidence: 99%

A Novel Statistical Method for Scene Classification Based on Multi-Object Categorization and Logistic Regression

Ahmed

Jalal

Kim

2020

Sensors

100

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In recent years, interest in scene classification of different indoor-outdoor scene images has increased due to major developments in visual sensor techniques. Scene classification has been demonstrated to be an efficient method for environmental observations but it is a challenging task considering the complexity of multiple objects in scenery images. These images include a combination of different properties and objects i.e., (color, text, and regions) and they are classified on the basis of optimal features. In this paper, an efficient multiclass objects categorization method is proposed for the indoor-outdoor scene classification of scenery images using benchmark datasets. We illustrate two improved methods, fuzzy c-mean and mean shift algorithms, which infer multiple object segmentation in complex images. Multiple object categorization is achieved through multiple kernel learning (MKL), which considers local descriptors and signatures of regions. The relations between multiple objects are then examined by intersection over union algorithm. Finally, scene classification is achieved by using Multi-class Logistic Regression (McLR). Experimental evaluation demonstrated that our scene classification method is superior compared to other conventional methods, especially when dealing with complex images. Our system should be applicable in various domains such as drone targeting, autonomous driving, Global positioning systems, robotics and tourist guide applications.

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Section: Overview Of Solution Frameworkmentioning

confidence: 99%

A Novel Statistical Method for Scene Classification Based on Multi-Object Categorization and Logistic Regression

Ahmed

Jalal

Kim

2020

Sensors

100

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“…During Helbert transform, we identified the minimum level of frequency retained by calculating the Fourier transform of the given signal a ( t ) to discard the negative frequencies and to double the magnitude of positive values [ 23 ]. These outputs become the complex-valued signals in which imaginary and real part values form a Hilbert transform pair, as shown in Figure 5 .…”

Section: Designed Framework For Wearable Harmentioning

confidence: 99%

Wearable Inertial Sensors for Daily Activity Analysis Based on Adam Optimization and the Maximum Entropy Markov Model

Tahir

Jalal

Kim

2020

Entropy

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Advancements in wearable sensors technologies provide prominent effects in the daily life activities of humans. These wearable sensors are gaining more awareness in healthcare for the elderly to ensure their independent living and to improve their comfort. In this paper, we present a human activity recognition model that acquires signal data from motion node sensors including inertial sensors, i.e., gyroscopes and accelerometers. First, the inertial data is processed via multiple filters such as Savitzky–Golay, median and hampel filters to examine lower/upper cutoff frequency behaviors. Second, it extracts a multifused model for statistical, wavelet and binary features to maximize the occurrence of optimal feature values. Then, adaptive moment estimation (Adam) and AdaDelta are introduced in a feature optimization phase to adopt learning rate patterns. These optimized patterns are further processed by the maximum entropy Markov model (MEMM) for empirical expectation and highest entropy, which measure signal variances for outperformed accuracy results. Our model was experimentally evaluated on University of Southern California Human Activity Dataset (USC-HAD) as a benchmark dataset and on an Intelligent Mediasporting behavior (IMSB), which is a new self-annotated sports dataset. For evaluation, we used the “leave-one-out” cross validation scheme and the results outperformed existing well-known statistical state-of-the-art methods by achieving an improved recognition accuracy of 91.25%, 93.66% and 90.91% when compared with USC-HAD, IMSB, and Mhealth datasets, respectively. The proposed system should be applicable to man–machine interface domains, such as health exercises, robot learning, interactive games and pattern-based surveillance.

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“…QDA [20] was used to evaluate which feature values can distinguish between all activity classes in labeled datasets. Each class was dispersed normally [21], and therefore a quantification function for quadratic discriminant analysis was applied as…”

Section: Features Discriminationmentioning

confidence: 99%

Accurate Physical Activity Recognition using Multidimensional Features and Markov Model for Smart Health Fitness

2020

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Recent developments in sensor technologies enable physical activity recognition (PAR) as an essential tool for smart health monitoring and for fitness exercises. For efficient PAR, model representation and training are significant factors contributing to the ultimate success of recognition systems because model representation and accurate detection of body parts and physical activities cannot be distinguished if the system is not well trained. This paper provides a unified framework that explores multidimensional features with the help of a fusion of body part models and quadratic discriminant analysis which uses these features for markerless human pose estimation. Multilevel features are extracted as displacement parameters to work as spatiotemporal properties. These properties represent the respective positions of the body parts with respect to time. Finally, these features are processed by a maximum entropy Markov model as a recognition engine based on transition and emission probability values. Experimental results demonstrate that the proposed model produces more accurate results compared to the state-of-the-art methods for both body part detection and for physical activity recognition. The accuracy of the proposed method for body part detection is 90.91% on a University of Central Florida’s (UCF) sports action dataset and, for activity recognition on a UCF YouTube action dataset and an IM-DailyRGBEvents dataset, accuracy is 89.09% and 88.26% respectively.

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Influence of kernel clustering on an RBFN

Cited by 31 publications

References 22 publications

A Novel Statistical Method for Scene Classification Based on Multi-Object Categorization and Logistic Regression

A Novel Statistical Method for Scene Classification Based on Multi-Object Categorization and Logistic Regression

Wearable Inertial Sensors for Daily Activity Analysis Based on Adam Optimization and the Maximum Entropy Markov Model

Accurate Physical Activity Recognition using Multidimensional Features and Markov Model for Smart Health Fitness

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