Feature Extraction Using Wavelet-PCA and Neural Network for Application of Object Classification &amp;#x00026; Face Recognition

Chitaliya, Nehal; Trivedi, A.I.

doi:10.1109/iccea.2010.104

Cited by 26 publications

(14 citation statements)

References 7 publications

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“…The information about the edges are then used as input to a classifier for traditional house recognition. Besides edges, texture features such as Histogram of Oriented Gradients (HOG) is also used as features for object recognition [12], [13], [14]. Texture feature is less sensitive to illumination changes compared to edge feature.…”

Section: Introductionmentioning

confidence: 99%

Traditional house recognition

Sabri

İbrahim

Zulkifli

2015

2015 IEEE International Conference on Control System, Computing and Engineering (ICCSCE)

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This research investigates the performance of edge and texture features, and Artificial Neural Network (ANN) and Support Vector Machine (SVM) classifiers for traditional house recognition. Malaysia is well known for its wealthy segments of cultural heritage and one of it is the traditional Malay houses. Capturing images of houses from the natural scenes are affected by illumination changes, scale and rotation invariants that makes the recognition process very challenging. Feature and classifier selection plays an important role in this recognition process. Thus, two comparative studies have been conducted, that are, between edge and texture features and between ANN and SVM classifiers to determine which combination will produce better recognition performance. Experimental results show that edge features with ANN achieve good recognition result.

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Section: Introductionmentioning

confidence: 99%

Traditional house recognition

Sabri

İbrahim

Zulkifli

2015

2015 IEEE International Conference on Control System, Computing and Engineering (ICCSCE)

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“…PCA was used to reduce the dimensionality of the feature vector. The feature vector was utilized for classification based on Euclidean distance and neural network classifier [24]. Chitaliya et al also introduced an efficient method for face feature extraction and recognition based on contour let transforms and PCA.…”

Section: Related Workmentioning

confidence: 99%

Fault Detection and Classification with Optimization Techniques for a Three-Phase Single-Inverter Circuit

Gomathy¹,

Selvaperumal²

2016

Journal of Power Electronics

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Fault detection and isolation are related to system monitoring, identifying when a fault has occurred, and determining the type of fault and its location. Fault detection is utilized to determine whether a problem has occurred within a certain channel or area of operation. Fault detection and diagnosis have become increasingly important for many technical processes in the development of safe and efficient advanced systems for supervision. This paper presents an integrated technique for fault diagnosis and classification for open-and short-circuit faults in three-phase inverter circuits. Discrete wavelet transform and principal component analysis are utilized to detect the discontinuity in currents caused by a fault. The features of fault diagnosis are then extracted. A fault dictionary is used to acquire details about transistor faults and the corresponding fault identification. Fault classification is performed with a fuzzy logic system and relevance vector machine (RVM). The proposed model is incorporated with a set of optimization techniques, namely, evolutionary particle swarm optimization (EPSO) and cuckoo search optimization (CSO), to improve fault detection. The combination of optimization techniques with classification techniques is analyzed. Experimental results confirm that the combination of CSO with RVM yields better results than the combinations of CSO with fuzzy logic system, EPSO with RVM, and EPSO with fuzzy logic system.

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“…PCA summates large data sets by creating new vectors, called principle components that are a linear combination of the original data, which results in a reduction of the data's dimensionality. Therefore, PCA helps to reduce redundancy, filters noise in the data and compresses the data [8].…”

Section: Model Descriptionmentioning

confidence: 99%

Reducing data storage requirements for machine learning algorithms using principle component analysis

Kinkiri

Melis

2016

2016 International Conference on Applied System Innovation (ICASI)

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While current computers have shown to be particular useful for arithmetic and logic implementations, their accuracy and efficiency for applications such as e.g. face, object and speech recognition, are not that impressive, especially when compared to what the human brain can do. Machine learning algorithms have been useful, especially for these type of applications, as they operate in a similar way to the human brain, by learning the data provided and storing it for future recognition. Until now, there has been a strong focus on developing the process of data storage and retrieval, merely neglecting the value of the provided information and the amount of data required to store. Hence, currently all information provided is stored, because it is difficult for the machine to decide which information needs to be stored. Consequently, large amounts of data are stored, which then affects the processing of the data. Thus, this paper investigates the opportunity to reduce data storage through the use of differentiation and combine it with an existing similarity detection algorithm. The differentiation is achieved through the use of, Principal Component Analysis (PCA), which not only reduces the data storage requirements by about 80%, but also improves the overall detection accuracy around 50 to nearly 80%.

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Feature Extraction Using Wavelet-PCA and Neural Network for Application of Object Classification & Face Recognition

Cited by 26 publications

References 7 publications

Traditional house recognition

Traditional house recognition

Fault Detection and Classification with Optimization Techniques for a Three-Phase Single-Inverter Circuit

Reducing data storage requirements for machine learning algorithms using principle component analysis

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