Ontology-based Semantic Recognization And Retrieval Of Image

The semantic recognizing and retrieval of image has being a focus in computer academic field, which includes image processing, pattern recognizing, artificial intelligence, machine vision and so on. Some key problems are studied in this thesis, and some results are obtained.A four-layers image semantics model and recognizing framework are put forth. After a review on image semantics and recognizing, a four-layers model is introduced, which is composed of four layers from bottom to top: feature layer, entity layer, relationship layer, semantics layer. Then a semantics recognizing framework based on semantic network is proposed, which is composed of five parts: image segmentation, entity lib building, decision tree study, entity parsing, semantics parsing. The framework can do image segmentation and map region to entity based on decision rules. Semantic clarifying is also can be done by OMCSNet.Image entity template lib is built. According to Berkeley artificial segmentation lib, a java plugin is designed to obtain low level features based on ImageJ framework. For template image, the following features are produced: Lab color mean value; the entropy, contrast, energy and IDM of gray level concurrence matrix; Hu shape invariable moment.A region growing segmentation method is introduced which is based on adaptive anisotropic diffusing filtering. A diffusing control parameter is built to adaptive to the coarseness of image, which can make stronger diffusing effect in high-texture region. After adaptive anisotropic diffusing filtering, image become smooth, while the edge of region remain sharp. Then a region growing method is used to segment the image, which uses the combing distance of color distance and coarseness distance to merge small regions. Decision tree is built to generate decision rules.Image ontology is built, and semantics clarifying method is put forth based on OMCSNet. An ontology of image is built based on low level features description table. Space topological relationship of image ontology is defined by SWRL rules. To obtain the true meaning of region in image, OMCSNet is adopted to clarify the meaning of a region.