Comparing images is impractical and inefficient for large scale image retrieval. Wavelet transformation is a toll for processing images at multiple resolutions. In this project we use the discrete wavelet transform (DWT). The wavelet tool is an efficient, highly intuitive frame work for representation and storage of images. This tool provides insights into the image's spatial and frequency characteristics.
The wavelet transform is a tool is used for analyzing functions at different levels of detail. The DWT has a property of analyzing images at multiple resolutions. It is similar to the Fourier transform, but encodes both frequency and spatial information.
Wavelet tools can be used in a wide range of research on images such as in identifying pure frequency, de-noising, and image compression naming a few. In the project, wavelet transformation is used for decomposing images for retrieval of signature content (CSGV).
By saving the few largest wavelet coefficients for an image, it is possible to recover a fairly accurate representation of the image.
The Wavelet Transform
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| 20 coeffs | 100 coeffs | 400 coeffs | Original (16,000 coeffs) |
image source
The example shown above is used for image compression the third image from the left would require 3% of the of disk space as compared to the original image. In our project, we decompose upto 3 levels which results in a significant “signature content” and because the content is small enough, it allows a higher performance on searching for images in a large scale image database.
The Wavelet Toolbox (a collection of Math Works function of wavelet analysis) which is used in the implementation phase to decompose images to the user desired level.
