A novel object-oriented approach to image analysis and retrieval
Common image processing tasks such as quantitative analysis, classification, or image retrieval require content-based techniques to firstly detect visually perceivable structures that have a semantic interpretation for a specific observer in a certain context and secondly to describe their properties in a comprehensive way. To achieve these aims, we propose an object-oriented approach to image interpretation utilizing a morphological multiscale decomposition to transform an image into a hierarchical data structure that represents image objects by their topological relations and descriptive attributes. The object hierarchy can be stored in a relational image archive and serves as interface to a rule-based expert system that either evaluates image objects directly or compares them with those of the stored images. Thus, both image analysis and retrieval can be realized by appropriate queries to the expert system. The system has already been used successfully for quantitative analysis and classification of biomedical and aerial images.