Application Of Wordnet In The Image Semantic Analysis

Traditional content-based image retrieval (CBIR) systems often fail to meet a user’s need due to the’semantic gap’existed between the extracted features by the systems and the user’s query. In this paper we propose an approach to bridge the semantic gap which is the major deficiency of CBIR systems. We conquer such deficiency by extracting semantics of an image from the environmental texts around it.In this thesis, a new Wordnet semantic learning method to detect semantic region for image retrieval from a given amount of labeling effort is researched. In our approach, the database images are classified into two classes-the labeled class and the unlabeled class. Form images in the labeled class, we construct a concept detection to detect the important regions in each image based on the statistical information of a semantic class. All the images in the database are segmented into multiple disjoint regions, each of them is represented by three type of low-level visual features (i.e. color, shape, and texture). With this representation a region weighting model based on the statistical information of low-level visual features is predicted to analyze semantic concepts hidden in the database. One key obstacle in applying statistical methods to discover the hidden semantic concepts for annotating images in the amount of manually-labeled images is normally insufficient. For images that have not been annotated, the learning algorithm estimates their important regions whose low-level features are then extracted to retrieve semantic all similar image s form the test data base. Experimental results show that the performance of the proposed method is excellent as compared with that of simulated traditional content-based image retrieval.We used a Semantic analysis process, which adopts the Wordnet learning algorithm as a kernel, on the environmental texts of an image to extract the semantic information from this image. Some implicit semantic information of the images can be discovered after the Wordnet process. We also define a semantic relevance measure to evaluate these semantic-based image retrieval tasks.