Title :
High-Level Semantic Based Image Characterization Using Artificial Neural Networks
Author :
Ribeiro, Eduardo Ferreira ; Barcelos, Célia Aparecida Zorzo ; Batista, Marcos Aurélio
Author_Institution :
Univ. Fed. de Uberlandia, Uberlandia
Abstract :
Low-level attributes such as color, shape and texture generally fail in describing the high-level semantic concepts. This work presents, through the formation of a high- level characteristics vector, the representation of the subjective knowledge used by humans for the verification of which aspects are most important for image characterization. Such vector will be formed by using the Artificial Intelligence techniques, more specifically the Artificial Neural Networks, which will generate, through predefined examples, the low-level characteristics forming the new high- level vector, making image retrieval possible. Finally, some tests results are presented and discussed to demonstrate the potentiality of the method.
Keywords :
image colour analysis; image representation; image texture; neural nets; artificial intelligence techniques; artificial neural networks; high-level semantic based image characterization; image color; image retrieval; image shape; image texture; subjective knowledge; Artificial intelligence; Artificial neural networks; Computer networks; Content based retrieval; Humans; Image generation; Image retrieval; Information retrieval; Neural networks; Shape;
Conference_Titel :
Intelligent Systems Design and Applications, 2007. ISDA 2007. Seventh International Conference on
Conference_Location :
Rio de Janeiro
Print_ISBN :
978-0-7695-2976-9
DOI :
10.1109/ISDA.2007.89