Please use this identifier to cite or link to this item:
|Title:||A semi-Naïve Bayesian method incorporating clustering with pair-wise constraints for auto image annotation|
|Source:||Jin, W.,Shi, R.,Chua, T.-S. (2004). A semi-Naïve Bayesian method incorporating clustering with pair-wise constraints for auto image annotation. ACM Multimedia 2004 - proceedings of the 12th ACM International Conference on Multimedia : 336-339. ScholarBank@NUS Repository.|
|Abstract:||We propose a novel approach for auto image annotation. In our approach, we first perform the segmentation of images into regions, followed by clustering of regions, before learning the relationship between concepts and region clusters using the set of training images with pre-assigned concepts. The main focus of this paper is two-fold. First, in the learning stage, we perform clustering of regions into region clusters by incorporating pair-wise constraints which are derived by considering the language model underlying the annotations assigned to training images. Second, in the annotation stage, we employ a semi-naïve Bayes model to compute the posterior probability of concepts given the region clusters. Experiment results show that our proposed system utilizing these two strategies outperforms the state-of-the-art techniques in annotating large image collection.|
|Source Title:||ACM Multimedia 2004 - proceedings of the 12th ACM International Conference on Multimedia|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Dec 9, 2017
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.