| Tian Dongping (田东平).[J].高技术通讯(英文),2017,23(4):367~374 |
|
| Semi-supervised learning based probabilistic latent semantic analysis for automatic image annotation |
| |
| DOI：10.3772/j.issn.1006-6748.2017.04.004 |
| 中文关键词: |
| 英文关键词: automatic image annotation, semi-supervised learning, probabilistic latent seman-tic analysis ( PLSA), transductive support vector machine ( TSVM), image segmentation, image re-trieval |
| 基金项目: |
| Author Name | Affiliation | | Tian Dongping (田东平) | |
|
| Hits: 1175 |
| Download times: 1045 |
| 中文摘要: |
| |
| 英文摘要: |
| In recent years, multimedia annotation problem has been attracting significant research attention in multimedia and computer vision areas, especially for automatic image annotation, whose purpose is to provide an efficient and effective searching environment for users to query their images more easily.In this paper, a semi-supervised learning based probabilistic latent semantic analysis ( PL-SA) model for automatic image annotation is presenred.Since it' s often hard to obtain or create la-beled images in large quantities while unlabeled ones are easier to collect, a transductive support vector machine ( TSVM) is exploited to enhance the quality of the training image data.Then, differ-ent image features with different magnitudes will result in different performance for automatic image annotation.To this end, a Gaussian normalization method is utilized to normalize different features extracted from effective image regions segmented by the normalized cuts algorithm so as to reserve the intrinsic content of images as complete as possible.Finally, a PLSA model with asymmetric mo-dalities is constructed based on the expectation maximization( EM) algorithm to predict a candidate set of annotations with confidence scores.Extensive experiments on the general-purpose Corel5k dataset demonstrate that the proposed model can significantly improve performance of traditional PL-SA for the task of automatic image annotation. |
|
View Full Text
View/Add Comment Download reader |
| Close |
|
|
|
