Name
Abstract | ||
|---|---|---|
In this paper, we firstly propose an extended probabilistic latent semantic analysis (PLSA) to model continuous quantity. In addition, corresponding EM algorithm is derived to determine the parameters. Then, we apply this model in automatic image annotation. In order to deal with the data of different modalities according to their characteristics, we present a semantic annotation model which employs continuous PLSA and traditional PLSA to model visual features and textual words respectively. These two models are linked with the same distribution over all aspects. Furthermore, an asymmetric learning approach is adopted to estimate the model parameters. This model can predict semantic annotation well for an unseen image because it associates visual and textual modalities more precisely and effectively. We evaluate our approach on the Corel5k and Corel30k dataset. The experiment results show that our approach outperforms several state-of-the-art approaches. © 2012 IFIP International Federation for Information Processing. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1007/978-3-642-32891-6_42 | Intelligent Information Processing |
Keywords | Field | DocType |
aspect model,automatic image annotation,continuous plsa,image retrieval,semantic learning | Modalities,Automatic image annotation,Semantic annotation,Expectation–maximization algorithm,Computer science,Image retrieval,Semantic learning,Probabilistic latent semantic analysis,Natural language processing,Artificial intelligence | Conference |
Volume | Issue | Citations |
385 AICT | null | 0 |
PageRank | References | Authors |
0.34 | 14 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhixin Li | 1 | 111 | 24.43 |
| Zhiping Shi | 2 | 168 | 43.86 |
| Zhengjun Tang | 3 | 0 | 0.34 |
| Weizhong Zhao | 4 | 363 | 23.10 |