Artists pictures rarely have photo-realistic detail. Tools to create pictures from digital photographs might, therefore, include methods for removing detail. These tools such as Gaussian and anisotropic diffusion filters and connected-set morphological filters (sieves) remove detail whilst maintaining scale-space causality, in other words new detail is not created using these operators. Non-photorealistic rendering is, therefore, a potential application of these vision techniques. It is shown that certain scale-space filters preserve the appropriate edges of retained segments of interest. The resulting images have fewer extrema and are perceptually simpler than the original. A second artistic goal is to accentuate the centre of attention by reducing detail away from the centre. The process also removes the detail providing perceptual cues about photographic texture. This allows the ‘eye ’ to readily accept alternative, artistic, textures introduced to further create an artistic impression. Moreover, the edges bounding segments accurately represent shapes in the original image and so provide a starting point for sketches. 1

This paper explores a new method for analysing and comparing image histograms. The technique amounts to a novel way of backprojecting an image into one with fewer, statistically significant colours. When the method is tested with the COIL-100 and MPEG-7 data sets it is shown to have a performance that is as good as the best methods using fewer enteries than the original histogram. Therefore it offers the potential for extending the use of histograms into high dimensional feature spaces. 1

Color histograms are a central feature in many image retrieval systems. Indeed they are part of the MPEG-7 standard. But histograms suffer from the "curse of dimensionality " in which the number of bins increases exponentially with the number of dimensions. There is therefore an imperative for methods for simplifying histograms. This paper presents a new method for simplifying histograms based on a cascade of increasing-scale graph morphology filters. The system we choose preserves scale space causality and so preserves the modes of the histogram. The method is quick to compute so is therefore a practically useful feature. We present results using the MPEG-7 Common Color Dataset that show that these new compressed features have a retrieval performance that is equivalent to full histograms.

A new way of representing histograms is presented. The representation is based on a hierarchical scale-space decomposition that forms a scale tree. The tree is a mapping of the original histogram and provides a useful starting point for the visualization and analysis of high-dimensional histograms that are commonplace in image retrieval systems.