Large Scale Graph Evaluation for Find Communities in Big Data

Image segmentation is a key step in the process of image data processing. The quality of image segmentation will directly affect the accuracy of image cognitive understanding. The purpose of image segmentation is to divide the image into regions with specific semantics. For the simple linear iterative clustering (SLIC) algorithm, the feature equalisation parameters need to be set manually during image segmentation, which results in the lack of segmentation effects and slow processing time. By introducing the theory of intermediary mathematics, an improved adaptive SLIC super‐pixel algorithm is proposed, which can adaptive generate characteristic equalisation parameters according to the specific situation of the image, thereby simplifying the operation steps and improving the image segmentation effect. After experimental verification and analysis, compared with the original SLIC algorithm and several other super‐pixel contrast algorithms, the algorithm in this study can effectively shorten the processing time and achieve a better segmentation effect.

show abstract

Large Scale Graph Evaluation for Find Communities in Big Data

Cited by 2 publications

References 17 publications

Investigating Large-Scale Graphs for Community Detection

Investigating Large-Scale Graphs for Community Detection

Super‐pixel image segmentation algorithm based on adaptive equalisation feature parameters

Contact Info

Product

Resources

About