Pavement crack detection plays an important role in pavement maintaining and management, nowadays, which could be performed through remote image analysis. Thus, edges of pavement crack should be extracted in advance; in general, traditional edge detection methods don’t consider phase information and the spatial relationship between the adjacent image areas to extract the edges. To overcome the deficiency of the traditional approaches, this paper proposes a pavement crack detection algorithm based on spectral clustering method. Firstly, a measure of similarity between pairs of pixels is taken into account through orientation energy. Then, spatial relationship is needed to find regions where similarity between pixels in a given region is high and similarity between pixels in different regions is low. After that, crack edge detection is completed with spectral clustering method. The presented method has been run on some real life images of pavement crack, experimental results display that the crack detection method of this paper could obtain ideal result.

With the development of future Internet, it is of great significance to study how to realize unified management information modeling, in order to avoid a lot of repetitive work and standardize information modeling in network management domain. This paper discusses the problem from the ontology point of view and introduces the theory of concept lattices into the research on semantic management information modeling, which includes a) establishing an ontology-driven framework for semantic management information modeling, b) building unified management information modeling ontology based on concept lattices, and c) generating semantic models for network management information modeling using the theory of concept lattices.