Shape Extraction Framework for Similarity Search in Image Databases

Abstrakt The task of similarity search in image databases has been studied for decades, while there have been many feature extraction techniques proposed. Among the mass of low-level techniques dealing with color, texture, layout, etc., an extraction of shapes provides better semantic description of the content in raster image. However, even such specific task as shape extraction is very complex, so the mere knowledge of particular raster transformation and shape-extraction techniques does not give us an answer what methods should be preferred and how to combine them, in order to achieve the desired effect in similarity search. In this paper we propose a framework consisting of low-level interconnectable components, which allows the user to easily configure the flow of transformations leading to shape extraction. Based on experiments, we also propose typical scenarios of transformation flow, with respect to the best shape-based description of the image content.