Research On Description Of Image Semantic Models In Computer Vision

The research on the desceiption of image semantic models in computer vision is a very important work. When the computer assembled with vision function, it can help people in all fields. This vision aims to the solution of storage, process, description, expression and understanding image in 2D to 3D objects. The vision system includes based knowledge on Optics, Physics, Mathematics, Automatics and Computer Science. The research integrates each course to generate a new branch for vision. The computer will get the ability of sense, recognition and decision from the integrated.The paper presents a concept and application in computer vision with high knowledge. It has analysed the existing state of computer vision, causes and the demands of development in the future. Especially in the image processing of computer vision, the paper organized the basic knowledge of methods and principles on image processing. It also gave the experience on description, content, and the detail in computer vision of image processing. It can impress studying people a resume and outline about computer vision. Helping human to learn the knowledge about computer vision.In order to slove the problems of self-learning and understanding, computer vision should be constructed on high knowledge molds. The paper presents a method which using the knowledge of image semantics and theory. The semantic concept was introduced with connotation and evolution. This discuss includes the relationship between high knowledge and semantics, linking, category. The summery is a base to the word "semantics" which will be frequently used in computer vision.Semantics is a high description of image attributes. The description is a hierachical structure that contains three layers. This construction extracts the information from the image attributes, relation of objects in space and high information of image. A feature vector which be used in the recognition of vision can be extracted from the three layers. This vector was used to describe image in high knowledge. The semantics presents relations and structures of image and spectral, object and object, image and sensibility. The relation of objects reflects location and state. But the impression refers the image in the society. This research is to find the reason on image reserve and condition in high knowledge. Seeking a new field to serve the society using the rich image resource. This research is an advanced and necessity for the image application using a new idea and efficient results.The semantic definition and its concepts have been given in beginning of the paper. The related events with semantics also have been interpreted. Image semantics of computer vision is the means of changing the recognition from known to unknown environment. The description is the object that projects to the computer vision/camera in direction. The data was epurated from selected object or objects in image attributes. There is an eigenvalue that has been extracted by the projection of vector space. This discuss mainly connects with the basic application. The description was formed like the projection from image attributes/bottom to high information. The projection is the solution to how to select the image attributes and construct the semantics. In order to realize this, the SVM has been used to build the relation between the attributes and senior information. The principle and methods of generation also have been constructed followed by these definitions. This is the basic theory system of computer vision.In the paper, it also gives the related solutions that how to use the concepts of semantics and the digited description in the application of computer vision. The eigenvalue was given to represent the semantics to feature vector space. There are necessary to offer the pattern recognition and database. In order to describe the semantics, the symbols strings were used to represent the eigenvalue from semantic models. In the operation, the recognition of models and database have also been defined and settled. This work is useful to solve the presentation of semantics, category of concept, models of concept, process and steps. Data has been managed in database.The paper presents the representation of semantic models and semantics structures to heighten the efficiency and reliability of image semantic application. It also solved the key question of semantics storage in the computer. The presentation showed the related complex and experimental results. The semantic models, structure of semantic models and structure of semantic storage have been constructed in high knowledge. The structure of storage was built in cross link-list. This strategy comes from the application and the concept-self. The description of third sects was presented in eigenvalue and characters. The structure reflects the feature of semantics. The link-list can easy realize the linked operation and semantic operation between different semantics. The cross link-list can present the relation and knowledge expression. This storage structure offers a stage for self-study and reform.The rules of semantic generation were constructed for getting semantics. Basedon the rules, generating a semantic just like to extract the eigenvalue from the feature tree. The semantic tree is called description of model(TDM). The prior of depth and clustering were considered to get the semantics from the TDM has been suggested. The threshold M is also considered to use in the clustering to reduce numbers of element. These made the process of generating semantics clear and sample. This work helps people to analyse and find errors in the semantics. The similar computation has been designed to offer the degree for the object in semantic recognition.The paper presents the knowledge operation of image semantic models to solve the problems which how to show the relation between the semantics. From the construction of semantic knowledge and feature vector, the structure can be defined and to be used in semantic operation. In order to manage and operate the knowledge structure, the semantic models and operations have been classified. The operation BNF structure has also been defined. The related degree calculates the relation among the semantic models.There are two kinds of semantic experiment. This strategy is to divide the solution into two parts. The semantic models were designed to bridge image and high knowledge. The semantic application began with the semantic models. Then the experiment constructed the description in high knowledge based on the semantic models. From the experiment, track of human motion and track of road automobile, the result showed the two kinds would be easy accepted and realized. The results compared with that of general method that did not used the semantic model is wonderful. Based on the above, the high knowledge of semantics was efficient to construct eigenvalue in the emulational experiment. The results present the innovative and advantaged of semantic application.Based on the summary, there are some results and prediction for the semantics study and application in the future.The paper has constructed and added some description for computer vision in knowledge structure. This point is to aim the high knowledge to solve some problems in image retrieval and recognition in the computer vision. This work is significance in theory and application. The study exploits a new description for computer vision.