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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
that are more similar are considered to be entries of a dictionary associated with the initial keyword used for the query. Moreover, the corresponding regions are parts of the visual lexicon describing the keyword. Also, an already existing lexicon may be iteratively updated by new features that may not match the existing
, 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
This paper presents a new method of automatic image annotation based on visual cognitive theory that improves the accuracy of image recognition by taking two semantic levels of keywords that give feedback to each other into consideration. Our system first segments an image and recognizes objects in the K-Nearest
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
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