Abstract: Image classification forms the basis for computer vision which is a trending sub-field in Machine Learning. The Convolutional Neural Network (ConvNet) has recently achieved great success in many computer vision tasks. The common architecture of ConvNets contains many layers to recurrently extract suitable image features and feed them to the softmax function for classification which often displays low prediction performance. In this paper, we propose the use of K-Nearest Neighbor as classifier for the ConvNets and also introduce the use of Principal Component Analysis (PCA) for dimensionality reduction. When successfully implemented, the proposed system should be able to accurately classify images.

Keywords: Image classification, Convolutional Neural Networks, Principal Component Analysis, K-Nearest Neighbor

PDF | DOI: 10.17148/IJARCCE.2018.71201

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