| dc.description.abstract | Query results not always match with user's desire. The key issue in relevance feedback is how to utilize the feedback information to improve the retrieval performance. This research proposed a method of relevance feedback using the Bayesian network model. Research data consists of 550 images which are grouped into 11 classes. The information concern in this model are histogram-l 62 for color infonnation, gradient for shape information and co-occurrence matrix for texture infOlmation. This research used Rocchio as relevance feedback algorithm. Re-weighting in feature vector used inter-weight and intra-weight updating theory. The weights that have been updated then it was used as posterior probability, and also used as a rank ofrelevant image. Evaluation of retrieval result is computed using average value of precision, the results showed that average value of precision for all classes is increasing about 4.7959% on Bayesian network model with relevance feedback compared with Bayesian network model without relevance feedback. Through the results, this research can improve the retrieval perfonnance in CBIR. | en |
