Research On Image Understanding Algorithm Based On Semantic-Binding Hierarchical Visual Vocabulary

With the development of the technology of internet and multi-media, the application of Digital Image has been fully spread through the social life.And at the meantime, with the continuous progress and innovation of the hardware and software facility, Computer Science is booming on its way. Under such circumstances, the research on Image Understanding has become one of the hottest points in field of Computer Vision. The Image Understanding is mainly responsible for recognizing the semantic and content of images just like what human beings do with the help of the proposed models and algorithm which is running on computers. The theory from the research on Image Understanding has been widely applied into the society, including the field of medical treatment, security control, military technology and etc. In recent years, More light has been shed on the research of Image Understanding since the need and range of its application is widened all the time.This papger firstly proposed the concept of Hierarchical Semantic Model on the conclusive analysis of recent research work on Image Understanding globally.Hierarchical Semantic Model can construct a semantic model in which semantic is connected with other semantic located at contiguous layers through the analysis on all the image semantic from the semantic space. The definition of the semantic connection and semantic attributes will be given out when the Hierarchical Semantic Model has been introduced.After the introduction of Hierarchical Semantic Model, the concept of Semantic Binding Hierarchical Visual Vocabulary (SBHV) will be proposed, and also the method to construct the SBHV and some certain details will be discussed afterwards.SBHV is a kind of visual vocabulary which is coustructed on the template of Hierarchical Semantic Mode with the SIFT (Scale-Invariant Feature Trasform) image feature.SBHV is made up of several layers of sub-vocabulary which is responding to one certain image semantic.After that, the comparative analysis with traditional BOVW model will be generated.This paper will apply the Hierarchical Semantic Model and SBHV into two kinds of concrete Image Understanding problems: 1) image content recongnition based on semantic; 2) image retrieval basec on semantic. The method to merge the proposed model and algorithm into the solutions of the above two kinds of problems will be discussed. And at the mean time, experiments on applying the SBHV into the solutions of above problems and on comparison with the tranditional model and algorithm is carried out to confirm the innovation and effectiveness of model and algorithm proposed by this paper.