Multiscale Pyramid Structure
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The module allows retrieval of similar images based on the general distribution of brightness in the image and sub-images. It is only suitable for monochromatic images, such as black and white scans from IR-reflectance, or similar. Colour images are converted to monochrome before comparison using this module.
This module divides database images into a heirarchy of small patches and the monochrome histogram is calculated for each patch. The query is compared with all patches so that sub-images may be located in the parent image.
The module is good for retrieval of images based on a known query image if the retrieved images
are required to contain an area with a similar distribution of brightness to the query image. This means
it is also suitable for retrieval of images based on a query image which is only part of a complete image.
Module Properties
| Module Speed | Slow |
| Module Accuracy | High |
The detail finder finds sub-images by dividing the query and the database image into a number of tiles over a number of resolutions calculating the Mono histogram for each of the tiles.
Multiscale Pyramid Structure |
The highest resolution image is converted into 64x64 tiles, overlapping by 32 pixels in each dimension. The image resolution is halved and, again, divided into 64x64 tiles (of which there will be 4 times less). The lowest resolution is of 64x64 pixels and 1 single tile.
For each tile a Mono histogram is created and stored, so that the final feature vector is a set of Mono histogram feature vectors, one for each tile.
Both the query image and the database image are converted into a pyramid structure, and then each of the tiles in the query image are compared against each of the features for the tiles in the database image using the Mono histogram matching algorithm, as described on the Mono histogram Help Page.
The query is converted to a pyramid to facilitate the database image being a subimage of the query image (double sub-image detection). An alternative is to assume the query is a subimage of the database image only, and perform a Mono histogram match of the whole query image against each of the tiles in the database image.
The following is an example query which would be considered good results.
Note: The matching is based only upon the general brightness levels of the images and subimages.
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| Query | 1 | 2 | 3 |
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| 4 | 5 | 6 |
The multi-scale monochrome histogram has the advantage over the multi-scale colour histogram in that the results can appear in both grey-level and colour images. The multi-scale colour histogram will only work with colour images. The results above show this, as the correct sub-image and sub-image location is found in the monochrome image, and also in the colour image.
Because the multi-scale monochrome histogram is a multi-scale version of the regular monochrome histogram, you can expect all the same types of properties and shortcomings. Used on its own you should definitely not expect the multi-scale monochrome histogram algorithm to be able to find specific instances of objects (e.g. chairs, pots, etc). However, when used with a metadata search to find similar objects, this algorithm could locate those of a simliar brightness distribution.