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Automatic Image Annotation and Retrieval

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dc.contributor.author Yu, May The
dc.contributor.author Sein, Myint Myint
dc.date.accessioned 2019-07-03T04:48:16Z
dc.date.available 2019-07-03T04:48:16Z
dc.date.issued 2011-05-05
dc.identifier.uri http://onlineresource.ucsy.edu.mm/handle/123456789/216
dc.description.abstract In this paper, an automatic image annotation and retrieval model is developed base on the intensity invariant approach. The given uncaptioned image is divided into background and foreground images and segmented into regions, which are classified into region types using a variety of features. Firstly, preprocessing stages such as gray-scale converting, noise filtering for image enhancing is processed. After segmentation, calculate the eigenvectors of images and examined the associated word by using database. The various types of images are applied for training and testing. The top words are described for annotated image in result. Manual image annotation is time-consuming, laborious and expensive; so, there has been a large amount of research done on automatic image annotation and retrieval technologies are combined to improve the performance. en_US
dc.language.iso en en_US
dc.publisher Ninth International Conference On Computer Applications (ICCA 2011) en_US
dc.subject Automatic image annotation en_US
dc.subject image retrieval en_US
dc.title Automatic Image Annotation and Retrieval en_US
dc.type Article en_US

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