Research On Ontology Based Image Retrieval

The progress of computer technology, multi-media technology and internet technology result in the explosive growth of image.How to fully make use of the image and get useful image is the hot research. Traditional image retrieval can not meet the needs understanding in the semantic level. We focus on ontology based image retrieval and the main contributions are following:1 .After surveying the research state of the content based image retrieval and semantic based image retrieval, an ontology based image retrieval framework is proposed which domain ontology is used. This framework covers the visual feature and semantic concepts. it can make fully use of image visual feature and conform to human visual understanding. Ontology not only can define the concept relation ,but also can make the query information insufficiency during the image retrieval.2.A method of information bottleneck is proposed to image segmentation, and it extract the image blobs using information bottleneck .Considering the two regions whose are the neighbour may in the same blobs , and the remote ones may belong to two difference image blobs ,an agglomerative information bottleneck method is applied to cluster image pixel which takes mutual information cost and the cluster region space distance for getting the better result. Comparing with other cluster methods, the cluster result doesn't rely on distance function and initialization cluster center. The experiments validate the feasibility and validity of the method proposed.3.An ontology based image automatic annotation algorithm is presenet. In training phase ,adopt semi-supervised information bottleneck algorithm to cluster the blobs .It use small marked samples to help unsupervised learning. During the clustering process ,it use some priori knowledge as constraints ,and can get more heuristic information, and improve the efficiency and cluster quality. A probability table of the blobs and ontology concept is constructed using statistics and semi-supervised learning.In automatic annotation phase , use two steps to annotate images, first get the image attribute concept using classification , then get the image concept from ontology.The experiments validate the feasibility and validity of the method proposed. 4.The image similarity model is defined using image ontology framework, and the similarity algorithm based Approximation vector is given; A rapid retrieval method based LPP VA-File is proposed under ontology framework, which construct VA-File in Locality Preserving Projections transform domain, eliminate the relativity of image feature vector, and keep the nonlinearity; KNN under LPP VA-File is present which can eliminate the accessing of original image feature , reduce the I/O time with orginal image, and retrieval efficiency is improved greatly. The experiments validate the feasibility and validity of the method proposed.5.A new relevance feedback method using SVM with priori knowledge is proposed in this paper. The training samples is defined with new weights using ontology priori knowledge ,which overcome the the deficiency of small samples in SVM training. The weight of samples reflect the ontology priori knowledge and users' interests. The higher the weight ,the more reliability the samples will have ,and they will have more effect in SVM. SVM with priori knowledge is proposed ,and the short relevance feedback and on which the long relevance feedback are proposed.