Feature extraction is an important part of any image classification scheme. It provides more informative and compact values derived from the original data. In this paper two conventional and widely used techniques known as principal component analysis (PCA) and discrete wavelet transform (DWT) are used for feature extraction. Both techniques are based on entirely different approaches. The results for the two techniques are analyzed and compared. The classification is performed with a benchmark classifier support vector machine. The experiments are carried out on a publically available datasets. The results have shown that DWT has performed better than PCA under the tested scenario.

Classification, Feature extraction, Discrete Wavelet Transform (DWT), Principal Component Analysis (PCA)

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