Research On Semantic-based Image Retrieval With Relevance Feedback

With the constant development of multimedia technology, network technology and database technology, image is becoming a very important means of media representation. How to retrieve the needed resources from the mass of data is accordingly becoming one of the current research hotspots.Content-based image retrieval technology has made rapid progress in recent years, and it took a big step forward for image retrieval technology that relevance feedback technology went into this technology. However, content-based image retrieval and relevance feedback can't still fully reflect the features of high-level semantics, how to narrow the "semantic gap", and how to combine the semantic with the low-level features for better retrieval results, which are the current problems as well as the next few years.The dissertation put the focus on the research of semantic-based image retrieval as well as relevance feedback technology. It proposed a semantic extraction algorithm based on a semantic matrix, using feedback technology and the semantic concept, and it also proposed another relevance feedback algorithm based on the combination of semantic and region of interest(ROI), which improved the efficiency of the semantic retrieval. The main research are as follows:(1) The dissertation proposed an initial extraction and semantic retrieval method using the semantic keywords and semantic matrix which constituted by images. First of all, we quantified the image by the color, accordingly, we got the color of every block, which was mapped to the semantic keyword, and then we extracted semantic meaning of images through the feedback technology based on semantic matrix, finally, we retrieved the image from the image groups using this method. The result showed that this method could initially extract the semantic of images, and it could obtain better retrieval results for the certain types of images.(2) The dissertation proposed a means of semantic-based image retrieval based on relevance feedback and ROI. We added the element of ROI to the presentation of low-level features, and took advantage of the relation of semantic features to ROI, consequently, we improved the feedback process, and appropriately reduced the number of feedback, and the retrieval efficiency could be improved.(3) The dissertation designed an image retrieval system. The system fulfilled the two means above.