The Design And Implementation Of Image Retrieval System Based On Semantic Network

As the highly development of multi-media and Internet, lots of image data comes out. The image retrieval becomes an important research task. The content-based image retrieval tries to analyze the image content directly in order to extract the low-level features (color, texture, shape, spatial feature) which describe the image content, forming a vector which does for the image. Then the most similar image is dug out through running the similarity match arithmetic. By the relevant feedback technology, users try to evaluate if or not the checked-out picture is suitable, narrow the retrieval field and try again until the most similar picture is seeked out. The recall rate and precision rate is two parameters to evaluate the quality of the image retrieval system.The semantic image retrieval intends to extract the semantic feature of a picture. The semantic hierarchy model is composed of low-level layer, object layer and concept layer. The semantic expression of the image includes text-based method, traditional knowledge method and language description method. The image semantic extract has three key processes which are feature extract, object recognition and field-knowledge-based semantic extract. The semantic analyse method includes extracting semantic automatically, setting up semantic network and basing on semantic vector. A picture can be labeled by hand, semi-automatically, by divided layer and by words clustering.The semantic network can be taken as a three-element group. The relevancy is to show how informative the semantic network is. The logic structure of a semantic network is like a net, in which a group of pictures combined with a group of keywords. How a picture matches a keyword is judged by weight. An improved semantic network uses keyword clustering and image classification in order to make the network less complex and label the picture automatically.An image retrieval experiment system based on semantic network is built up, using relation database model to store the network. Both the entity-relation model and the relation table are designed. The relation table includes five tables which are image table, annotation table, image-annotation table, low-level-feature indexing table and low-level feature table. The database table of an improved semantic network adds a conjoint field both to image table and annotation table. The evolution of the semantic network has to accomplish three steps: the first step is to calculate and input the low-level feature of image; the second step is the initialization of the semantic network; the third step is to train the semantic network. The experiment system uses three search methods, each of which uses semantic retrieval, low-level retrieval and mixed retrieval separately. Four rounds of experiment are done to compare the recall rate and precision rate in different ways and analyse the performance of this system.