Title :
Study on a rough set approach to semantic image retrieval
Author :
Cui, Qingmin ; Li, Wangao
Author_Institution :
Sch. of Comput. Sci. & Eng., Henan Inst. of Eng., Zhenzhou, China
Abstract :
In this paper, semantic gap is a challenging issue in image retrieval. Firstly, in the process of constructing vector space model, the theory of Latent Semantic Indexing is introduced to mine the semantic information of images, and then, rough set theory is applied to retrieve and match the semantic feature of image database in the approximate space of tolerance rough set. Lastly, semantic image classification algorithm is implemented. Experimental results show that the performance of the classification is greatly improved.
Keywords :
approximation theory; data mining; image classification; image retrieval; indexing; rough set theory; visual databases; approximate space; image database; latent semantic indexing; semantic feature; semantic image classification; semantic image retrieval; semantic information mining; tolerance rough set theory; vector space model; Buildings; Dinosaurs; Horses; TV; image retrieval; semantic gap; tolerance rough set;
Conference_Titel :
Broadband Network and Multimedia Technology (IC-BNMT), 2010 3rd IEEE International Conference on
Conference_Location :
Beijing
Print_ISBN :
978-1-4244-6769-3
DOI :
10.1109/ICBNMT.2010.5705216
