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This paper is dedicated to revisiting image and video mining techniques from the viewpoint of image modeling approaches, which constitute the theoretical basis for these techniques. The most important areas belonging to image or video mining are: image knowledge extraction, content-based image retrieval, video retrieval, video sequence analysis, change detection, model learning, as well as object recognition. Traditionally, these areas have been developed independently, and hence have not benefited from some common sense approaches which provide potentially optimal and time-efficient solutions. Two different types of input data for knowledge extraction from an image collection or video sequences are considered: original image or symbolic (model) description of the image. Several basic models are described briefly and compared with each other in order to find effective solutions for the image and video mining problems. They include feature-based models and object-related structural models for the representation of spatial and temporal entities (objects, scenes or events).