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For solving "semantic gap" which exists between the low-features and the high-level semantic features and the fuzziness of users' comprehension, combining Ontology and Fuzzy sets, an universal image semantic description model based on fuzzy domain ontology (SDMFDO) is constructed. Ontology is a kind of model that is used to describe the concepts and the(More)
In order to distinguish normal tissues and abnormal pathological changes in the clinic diagnose and pathology, it is required to segment the medical images. The snake model is an important method of getting the contour of the object in the image segmentation. However, it has many defects in some fields such as concavity processing, local optimization,(More)
Small sample space target recognition is a difficult problem in applications because the limited training samples cannot lead to satisfactory recognition accuracy. Combined with novel compression perception theory, we propose a new space target recognition method based on compressive sensing. This method avoids the sophisticated image preprocessing and(More)
A new classification approach using random forest with label constraints is proposed to deal with the underutilization effectively of spectral and spatial information for hyperspectral image classification. Firstly, the principal component analysis extraction method is adopted, and the extended morphological profiles of the image are extracted from the(More)
Artificial immune recognition system (AIRS), as an efficient and successful computational intelligence method, has been widely used for classification. However, this method is seldom used for hyperspectral image classification due to its complexity. To address this problem, a class-specific model based on AIRS, named as Single Class Learning Network AIRS(More)