This paper presents a Bayesian network model for content-based image retrieval (CBIR). In the explanation and test of this work, only two images features (semantic evidences) are involved: color and shape (from gradients of directions). However, one of the main advantages of the proposed strategy is its easy extension to several evidences. Considering the precision with which the images are retrieved, to highlight the evidences that generate the best results, we have introduced the use of nonextensive entropy. This concept extends the Shannon's classic theory of entropy for information systems. Experimental results show that may be a link between the parameters of the Tsalli's nonextensive entropy and the precision with which the images are retrieved from the database. In some cases, we have obtained up to 30% in terms of average precision.
Using Tsallis entropy into a Bayesian network for CBIR
IEEE International Conference on Image Processing 2005 ; 3 ; III-1028
01.01.2005
236553 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
Using Tsallis Entropy into a Bayesian Network for CBIR
British Library Conference Proceedings | 2005
|Dominant Color Region based Indexing for CBIR
British Library Conference Proceedings | 1999
|Qatris iManager: a general purpose CBIR system
British Library Online Contents | 2015
|British Library Online Contents | 2007
|