Abstract— In this paper, an approach to recognize object efficiently is presented based on empirical wavelet features. In many computer vision applications, object recognition is required and it is a challenging task due to size and orientation of objects in the image. The proposed approach uses Empirical Wavelet Transform (EWT) to extract the characteristic of objects in an image. From the components of EWT, energy and entropy features are extracted. Then K-nearest neighbor classifier is used to recognize the object in the given image. The results show that the fusion of energy and entropy features provides better classification accuracy of 99.81% where the energy and entropy features provide 98.42% and 98.97% respectively on the benchmark object database named Columbia Object Image Library Dataset (COIL-100).
Keywords— Object recognition, Empirical wavelet transform, energy features, entropy features, KNN classifier.
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