Abstract:

OBJECTIVE: To introduce models and core techniques of image data mining.
METHODS: Original images cannot be directly used for image data mining, which must be pretreated to generate image feature libraries for high layer mining. An image mining system should include functions of image storage, preprocessing, retrieval, mining, display, etc. It is mainly related to image data mining models and image data mining techniques.
RESULTS: MultiMediaMiner is an image data mining system developed based on DBMiner system and C-BIRD system, which is a typical feature-driven model. In information-driven model, pixel layers and object layers mainly process images, recognize objects, extract features. Semantic concept layers and mode knowledge layers mainly execute image data mining and knowledge integration. Information-driven model not only can execute mining in high layer of image, but also can be extended to allow mining at every layer and different layers. The core techniques of data mining based on images are related to: image processing techniques, such as removing noise, contrast enhancement, image segmentation, etc.; feature extraction and optimization techniques; classification, rule extraction, forecasting, clustering, etc.
CONCLUSION: Image data mining is a branch of data mining in theory, but because of the complexity of mining objects, image data mining is not a simple application and extension of traditional data mining theories and techniques in image data. Image data mining is a new field of research which has its own distinctive research contents, theories and technical frameworks.