Paper Title
Normalized Singular Wavelet Feature For Pattern Recognition

In the process of pattern recognition, feature descriptors are of major importance. Among various approaches of feature representation, wavelet based feature representation in spectral domain, is of greater importance. In the representation of wavelet features, wavelet resolution coefficients are extracted using set of filter banks and the obtained spectral bands are used for feature extraction based on the descriptive features. However the accuracy of these feature representation purely depends on the spectral information it reveals. The suitability of wavelet features is more effective due to its finer resolution representation. However, it is observed that these features are very randomly distributed and finer variations were mostly suppressed by the coefficients having high variation. In the process of pattern recognition, these finer features also hold information, which affects the retrieval accuracy. With this objective in this paper a normalized feature for wavelet feature extraction is proposed. Index Terms— Wavelet features, Normalized singular features, pattern recognition.