Name
Title | ||
|---|---|---|
A semi-naïve Bayesian method incorporating clustering with pair-wise constraints for auto image annotation |
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. |
Year | DOI | Venue |
|---|---|---|
2004 | 10.1145/1027527.1027605 | ACM Multimedia 2001 |
Keywords | Field | DocType |
pair-wise constraint,novel approach,main focus,annotation stage,language model,auto image annotation,experiment result,training image,region cluster,large image collection,bayes model,bayesian method,image annotation,posterior probability | Data mining,Automatic image annotation,Annotation,Pattern recognition,Naive Bayes classifier,Segmentation,Computer science,Posterior probability,Artificial intelligence,Cluster analysis,Language model,Bayes' theorem | Conference |
ISBN | Citations | PageRank |
1-58113-893-8 | 7 | 0.84 |
References | Authors | |
10 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wanjun Jin | 1 | 41 | 3.29 |
| Rui Shi | 2 | 298 | 28.07 |
| Tat-Seng Chua | 3 | 11749 | 653.09 |