 Decomposition in image domain |
 Decomposition in frequency domain |
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The module allows retrieval of similar images based on the general texture distribution of the image where image texture here, is concerned with the repeating patterns throughout the whole image.
The module is good for retrieval of images based on a known query image if
the retrieved images are required to have a similar texture. It
is not suitable for retrieval of images based on a query image which is only part
of a complete image.
Module Properties
| Module Speed | Very Fast |
| Module Accuracy | Medium |
The PWT decomposes an image based on a wavelet transform, which can be thought of as similar to a Fourier transform, which transforms the iamge domain into a frequency domain. The frequency components of the image are analysed and a number of descriptors generated which represent the amounts of a discrete number of frequencies in the image.
Decomposition in image domain |
Decomposition in frequency domain |
Images are resized to 512x512 to perform this decomposition, which yields 22 frequency descriptors for an image. This makes the matching very fast. The comparison is achieved using standard Euclidean distance measure:
where HQ and HM are the harmonics in the query and match feature vectors.
The following is an example query giving what would be considered good results.
Note: The matching is based only on the texture, i.e. repeating patterns, in the whole image.
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| Query | 1 | 2 | 3 |
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| 4 | 5 | 6 |
The above results show the type of results you could expect to get from the PWT. The dataset contains a set of fabrics, and the simlarity between the repeating pattern of the query fabric shows up in the results.
You should not expect the PWT to be able to find small amounts of texture, in an image, because it is not multi-scale. So a pot with the surface of a certain texture, may not find other pots in a dataset with the same surface due to the other information (such as backgrounds) in the image causing errors in the matching.
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| Query | 1 | 2 | 3 |
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| 4 | 5 | 6 |
Used on its own you should definitely not expect the PWT algorithm to be able to find specific instances of objects (e.g. chairs, pots, etc). However, when used with a metadata search to locate similar types of object, this algorithm could locate those of a similar texture.
