Research Of Image Skeletonization Representation Based On Stochastic Grammar

Object representation and recognition techniques are kemel issues in the image analysis and understanding,in which the appropriate object representation is the groundwork and different representation forms will result in different recognition strategy.The result of content based image retrieval is dissatisfactory due to the lack of structural representation.To solve this problem,song-chun zhu and David Mumford proposed to express object by embedding a stochastic graph grammar in an And-Or graph,and unified a number of popular models in the literature,such as Markov random fields and sparse coding with wavelets to realize grammatical inference,but method is lack of semantic information between the image.primitives.The representation and recognition approaches based on skelet on are recently paid more attention since skeleton has following characteristics:hierarchical, multi-scale,topology of uniformity and adaptability of variety.This paper combines the advantages of stochastic graph grammar and the skeleton,and proposes ideal using stochastic graph grammar to express skeleton structure,so it will help increase the semantic features between the image primitives and improve the content-based image retrieval accuracy.This paper main work concentrates in:(1)Aimed at the appearance of burr in the skeleton extraction algorithm,especially the ligature which has a significant impact on object shape descriptions,proposing a skeleton pruning algorithm based on the skeleton weight.(2)Combines the advantages of stochastic graph grammar and the skeleton, proposing a model SGIRS(stochastic grammar on image representation based on skeleton).(3)Taken advantage of flexibility of stochastic graph grammar and anti-interference ability of skeleton,proposing a object identification framework based on SGIRS and the experiment on two shape librarys given by Mpeg-7-test,the results show that this method better than not considered the weight of skeleton at reducing the probability of loss main shape for objective.