DocumentCode :
3452400
Title :
Image annotation based on manifold structure by fusion of multiple dissimilarity spaces
Author :
Chahooki, Mohammad Ali Zare ; Charkari, Nasrollah Moghadam
Author_Institution :
Fac. of Electr. & Comput. Eng., Tarbiat Modares Univ., Tehran, Iran
fYear :
2012
fDate :
2-3 May 2012
Firstpage :
261
Lastpage :
265
Abstract :
Automatic image annotation has been an active research topic in recent years due to its potential impact on both image understanding and web image retrieval. In many situations the similarity between two image feature vectors could not be found correctly by the Euclidean distance between feature vectors. The purpose of this study is to reduce the semantic gap in automatic image annotation by learning the intrinsic structure collectively revealed by known labeled and unlabeled images.We learn a semantical dissimilarity graph based on fusion of dissimilarities in multiple spaces. The experiments showed that the geodesic distances between the samples on the learned manifold structure are closer to their semantic distance. So, the continuity between the instances of a semantic at the semantic space is kept in manifold space. The proposed method has been compared to the other well-known approaches by Corel data set. The results confirmed the effectiveness and validity of the proposed method.
Keywords :
content-based retrieval; graph theory; image fusion; image retrieval; learning (artificial intelligence); semantic Web; AIA; CBIR; Corel data set; Euclidean distance; Web image retrieval; automatic image annotation; content-based image retrieval; dissimilarity fusion; geodesic distances; image feature vectors; image understanding; intrinsic structure learning; labeled images; manifold structure; multiple dissimilarity space fusion; semantic distance; semantic gap; semantic space; semantical dissimilarity graph; unlabeled images; Adaptation models; Feature extraction; Histograms; Image retrieval; Manifolds; Semantics; Training;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Artificial Intelligence and Signal Processing (AISP), 2012 16th CSI International Symposium on
Conference_Location :
Shiraz, Fars
Print_ISBN :
978-1-4673-1478-7
Type :
conf
DOI :
10.1109/AISP.2012.6313755
Filename :
6313755
Link To Document :
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