A Comparison between K-Means, FCM and ckMeans Algorithms

Vargas, Rogério Rodrigues de; Bedregal, Benjamín R. C.; Palmeira, Eduardo Silva

doi:10.1109/weit.2011.28

Cited by 8 publications

(1 citation statement)

References 17 publications

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“…The convolution, excitation, and pool layers are commonly included in the hidden layer, which is made up of numerous neurons and connections between the input and output layers. Numerous activation functions should be employed if the CNN has multiple hidden layers [12].…”

Section: Related Workmentioning

confidence: 99%

A New Improved Binary Convolutional Model for Classification of Images

Hemalatha

Shankar²,

Raj

2022

SCPE

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There are numerous image classification strategies are developed in deep learning. However, due to the complexity of images, conventional image classification strategies have been incapable to meet real application needs. As the amount of pixel information rises, the classification becomes more difficult. However, CNN is widely used method for object identification in picture due to its simple and accurate, but still, it remains hazy which strategies are most supportive for analysing and distinguishing the objects in pictures. In this paper we introduced a CNN network and clustering-based technique called IBCNN to perform classification based on patch extraction. The proposed method can accomplish their goals in the following four different ways: a) Automatic Kernel selection; b) resilient patch size selection; c) CNN layer; and d) pooling layer modification. In addition, it also modifies the pooling layer with average value and calculate the pixel size. The proposed method was applied on ten different image datasets. Finally, the proposed model is compared to three benchmarking models: such as WCNN, MLP, and ELM-CNN to estimate its performance. The obtained results shows that the proposed method gives competitive results compared to the other models.

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Section: Related Workmentioning

confidence: 99%