A new object recognition framework based on PCA, LDA, and K-NN

Hagar, Mohamed; Alshewimy, Mahmoud A. M.; Saidahmed, Mohamed T. Faheem

doi:10.1109/icces.2016.7821990

Cited by 7 publications

(2 citation statements)

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“…A colour vector representation model for object recognition is described in [11]. It uses Principal Component Analysis and Linear Discriminant Analysis to extract features from the data to generate the colour Eigenspace.…”

Section: Related Workmentioning

confidence: 99%

Bendlet Transform Based Object Detection System Using Proximity Learning Approach

Ramalingam¹,

H²

2022

IJASIS

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This study presents a Bendlet Transform-based Object Detection (BTOD) system that recognizes an object in the image. Finding a specific object in images or videos is the goal of the field of object recognition. Though humans are able to identify a large number of objects, it is very difficult for computer vision systems in general. The appearance of the objects may change depending on the perspective, the size or scale, or translation and rotation. This work extracts Bendlet transform-based features from the images at different levels, and then the discriminant features are selected by employing genetic algorithms. The performance of the BTOD system is analyzed with different nearest neighbours for classifying objects in the Columbia Object Image Library (COIL-100) in terms of classification accuracy. It is observed from the results that the BTOD system with a one-nearest neighbour provides better performance than the two-nearest neighbour classifier. The former classifier gives 99.47% accuracy, whereas the later classifier gives 99.19%.

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Section: Related Workmentioning

confidence: 99%

Bendlet Transform Based Object Detection System Using Proximity Learning Approach

Ramalingam¹,

H²

2022

IJASIS

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“…where X represents the mean value of sample vector X (i) and l represent the number of features. As a principal, the first eigenvalues ( i ) of the eigenvector matrix gives the direction of the maximum spread of the data [21]. So, the largest k eigenvalues (principal components) of the covariance matrix are chosen to create the matrix U reduce .…”

Section: Pcamentioning

confidence: 99%

Fault detection and classification in smart grids using augmented K-NN algorithm

Hosseinzadeh

Masoodzadeh

Roshandel³

2019

SN Appl. Sci.

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The ability of artificial intelligence and machine learning techniques in classification and detection of the types of data in large datasets lead to their popularity among scientists and researchers. Because of the presence of different load at different times in power systems, it is hard to provide an accurate mathematical model for such systems. On the other hand, most of the available protection devices in power grids work based on the estimated mathematical models of the grid. For this reason, power system utilizers usually suffer from the low accuracy of the available protection systems in fault detection and diagnosis. In this paper, a reliable machine learning technique is proposed to detect and classify different faults of smart grids. The proposed technique benefits from the principal component analysis (PCA) and linear discriminant analysis (LDA). The PCA is used to reduce the size of the dataset matrixes. The applied PCA reduces the dataset sizes and eliminates the possible singularity of the datasets. The LDA method is applied to the outputs data of the PCA to minimize the within class distance of the dataset and maximize the distance between classes. Finally, the well-known K-nearest neighbor technique is applied to detect the fault and determine its classes. The paper results demonstrate the effectiveness and robustness of the proposed algorithm in the determination of the fault class in smart grids.

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