We show a method that allows the pixel data of a set of images to be analyzed independently of any set of computed features. If the high and low aesthetic value images can be separated in the high dimensional space of pixel intensities then for any given set of features computed from the images, those features relevant to high aesthetic value can be determined and the range of feature values that correlate with high aesthetic appeal can be quantified. The method uses the Self Organizing Map to project raw pixel data of images onto a feature map. The aesthetic class of these images is overlayed on the feature map, yielding a semantic map. Average feature values are visualized in gray-scale heat maps and features relevant to aesthetic value are identified. We call this the Pixel Array Self Organizing Map (PASOM) method. For the set of images analyzed, brightness and texture features were identified as being discriminatory between images of high and low aesthetic value. High aesthetic value images tend to have higher brightness and richer textures. These findings were corroborated by a professional artist/photographer as being consistent with the principles for attaining aesthetic value in visual media. The PASOM method yields a semantic map and a visualization of feature value variation that together make possible a detailed analysis of features associated with the aesthetic value of images.

We describe an approach to generating animations of drawings that start as a random collection of strokes and gradually resolve into a recognizable subject. The strokes are represented as tree based genetic programs. An animation is generated by rendering the best individual in a generation as a frame of a movie. The resulting animations have an engaging characteristic in which the target slowly emerges from a random set of strokes. We have generated two qualitatively different kinds of animations, ones that use grey level straight line strokes and ones that use binary Bezier curve stokes. Around 100,000 generations are needed to generate engaging animations. Population sizes of 2 and 4 give the best convergence behaviour. Convergence can be accelerated by using information from the target in drawing a stroke. Our approach provides a large range of creative opportunities for artists. Artists have control over choice of target and the various stroke parameters.

We describe the implementation of an art installation that generates animated photomosaics of the viewer. Photomosaics are target images composed of smaller images known as tiles. When a photomosaic is viewed from afar the detail of the tiles is lost and the target image is evident. Up close, the opposite occurs: the detail of the tiles is evident and the target image is lost. Our system uses a photo of the viewer as the target and miniatures of the viewer's face as tiles. Evolutionary search is used to find the best selection and arrangement of tiles. Each newly found best image is then used as the frame of a movie. The resulting animations start from a random arrangement of tiles and gradually the viewer's face emerges and is clearly visible, and then gradually de-materialises into a random pattern.