Wavelet analysis found to have a variety of applications. While other transforms such as the DCT may achieve a better compression ratio, their rule of zeroing small coefficients is applied evenly and globally while in wavelet analysis, the rule of zeroing can be applied locally, preserving small coefficients that account for important minute features (such as in fingerprints). Starting with an introduction to wavelet analysis and some related concepts useful for classification the book provides a coverage of the theory and mathematical foundations of wavelets, the Best Basis, the Joint Best Basis, Principal Component Analysis and the Approximate PCA as well as the application of these tools to derive feature vectors for the classification of mammographic images.

This book will be useful as a reference text and will benefit both the audience whose interest is at the conceptual level, as it provides a qualitative description of the underlying ideas of wavelet theory and the audience who is interested also in the theory and mathematical foundations of wavelet analysis and its applications.

]]>In [2] a new strategy is suggested based on wavelet decomposition of the query image and the database images combined with a metric which is designed to be insensitive to small differences in the query process. This approach is found to be fast and overcomes the above mentioned problems.

Wavelet coefficients of an image may be very strongly when the image is displaced or rotated (unlike color histogram of an image which is invariant under displacement and rotation). Although the metric suggested in [2] is more robust to these errors when compared to the L1 and L2 metric (but worse when compared to the metric based on color histograms), still the error is significant.

In this paper we provide some experimental data on the sensitivity of the wavelet coefficients to displacement and rotation in the context of the standard characters and suggest an integration of the Hotelling transform to improve on this sensitivity. We also provide some experimental data on the distribution of the largest wavelet coefficients at different levels of the wavelet decomposition and discuss some questions relevant to the approach of using a set of the largest wavelet coefficients for image query.

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