We present a novel image filter generation method and an image filter retrieval algorithm and analyse their properties. Based on an original image and a filtered version of the original, the retrieval algorithm can find, to a high probability, which filter was applied to the original from a large pre-defined list of filters, without having to apply all filters to the original image, which is usually a time consuming task when the number of filters is large. This is achieved by pre-computing image annotations for a set of filtered images obtained by applying the pre-defined filters to a database of 50 images. Using standard imagebased annotation techniques, we show that the filter retrieval can be achieved by taking the closest images to the original from the database and analysing those known images instead. The retrieval algorithm has a set of parameters and we present results of experiments with these values to maximise the probability of retrieving the correct filter.
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