Automatic image segmentation and classification using on-line shape learning
Kyoung-Mi Lee, W. Nick Street · 2002
The detection, precise segmentation and classification of specific objects is an important task in many computer vision and image analysis problems, particularly in medical domains. Existing methods such as template matching typically require excessive computation and user interaction, particularly if the desired objects have a variety of different shapes. This paper presents a new approach that uses unsupervised learning to find a set of templates specific to the objects being outlined by the user. The templates are formed by averaging the shapes that belong to a particular cluster, and are used to guide an intelligent search through the space of possible objects. This results in decreased time and increased accuracy for repetitive segmentation problems, as system performance improves with continued use. Further, the information gained through clustering and user feedback is used to classify the objects for problems in which shape is relevant to the classification. The effectiveness of the resulting system is demonstrated on two applications: a medical diagnosis task using cytological images and a vehicle recognition task.