The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
Automatic image annotation (AIA) plays an important role and attracts much research attention in image understanding and retrieval. Annotation can be posed as classification problems where each annotation keyword is defined as a group of database images labeled with a semantic word. It is shown that, by establishing
, the improved model is capable of discovering the correlation between blobs (segmented regions) and textual keywords so as to automatically generate keywords for un-annotated image according to joint probabilities. Moreover, it has the ability to detect and remove false keyword(s) by considering the co-occurrence of
its relevance. During search, we retrieve similar images containing the correct keywords for a given target image. For example, we prioritize images where extracted objects of interest from the target images are dominant as it is more likely that words associated with the images describe the objects. We tailored our
Automatic image annotation is an important but highly challenging problem in semantic-based image retrieval. In this paper, we formulate image annotation as a supervised learning image classification problem under region-based image annotation framework. In region-based image annotation, keywords are usually
semantic analysis (LSA) is employed to the NN based annotation scheme (noted as LSA-NN) for discovering the latent contextual correlation among the keywords, which is neglected by many previous annotation methods. Instead of region-level as most previous works do, the LSA-NN based annotation scheme is built at image-level to
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.