Title :
Graphic retrieval based on limited semantics
Author :
Li, Hanjing ; Zhao, Tiejun ; Li, Sheng
Author_Institution :
Compt. Sci. & Technol. Sch., Harbin Inst. of Technol., China
fDate :
30 Oct.-1 Nov. 2005
Abstract :
Online graphic resources have grown rapidly. Methods for graphic retrieval have likewise improved including semantics-based graphic retrieval and content-based graphic retrieval. For content-based graphic retrieval users must input a draft drawing or sample image. For some tasks, such as converting text to scene, it is impossible to provide a draft. Presently semantics-based graphic retrieval is dependent on previously tagged details about the graphic. We propose a method based only on the semantic and morph similarity of the graphic file name which does not need additional graphic specifications. Experimental results have demonstrated the effectiveness of our method.
Keywords :
computer graphics; content-based retrieval; content-based graphic retrieval; draft drawing; graphic specification; morph similarity; sample image; semantics-based graphic retrieval; Animation; Databases; Frequency; Graphics; Information retrieval; Mice; Natural languages; Shape; Taxonomy; Graphic Retrieval; Semantic Database; Similarity;
Conference_Titel :
Natural Language Processing and Knowledge Engineering, 2005. IEEE NLP-KE '05. Proceedings of 2005 IEEE International Conference on
Print_ISBN :
0-7803-9361-9
DOI :
10.1109/NLPKE.2005.1598795