Name
Title | ||
|---|---|---|
Discriminative Structure Learning for Semantic Concept Detection With Graph Embedding |
Abstract | ||
|---|---|---|
Semantic concept detection is a very promising way to manage huge amounts of personal contents. In this paper, we propose discriminative structure learning for semantic concept detection with graph embedding. We focus on the task of whole-image categorization and employ graphical model inference based semi-supervised learning (SSL) to detect the semantic category of an image. To effectively extract global features from images, we utilize the spatial pyramid image representation. Then, we perform data warping over the histogram intersection kernel-based graph to learn discriminative features and make image distributions more discriminative for both labeled and unlabeled images. By data warping, each cluster of images is mapped into a relatively compact cluster as well as clusters become well-separated. Moreover, we adopt low-rank representation (LRR) in the embedded space to capture the global discriminative structure from the learned features for label propagation due to its good ability of capturing the global structure of data distributions and robustness against noise and outliers. Finally, we design a smooth nonlinear detector on the captured global discriminative structure to effectively propagate the concepts of labeled images to unlabeled images. Extensive experiments are conducted on four publicly available databases to verify the superiority of the proposed method compared to the state-of-the-art methods. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/TMM.2013.2291657 | IEEE Transactions on Multimedia |
Keywords | Field | DocType |
label propagation,graph embedding,image cluster,image representation,histogram intersection kernel-based graph,personal contents,discriminative structure,unlabeled images,inference mechanisms,learning (artificial intelligence),global feature extraction,ssl,visual databases,semantic concept detection,semi-supervised learning,data warping,semantic image category,low-rank representation,content management,lrr,smooth nonlinear detector,spatial pyramid image representation,feature extraction,image classification,graph theory,data robustness,image distributions,graphical model inference based semisupervised learning,publicly available databases,whole-image categorization,discriminative structure learning,learning artificial intelligence | Graph theory,Computer vision,Histogram,Pattern recognition,Feature detection (computer vision),Graph embedding,Computer science,Feature extraction,Artificial intelligence,Graphical model,Contextual image classification,Discriminative model | Journal |
Volume | Issue | ISSN |
16 | 2 | 1520-9210 |
Citations | PageRank | References |
4 | 0.43 | 26 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Meng Jian | 1 | 59 | 8.07 |
| Cheolkon Jung | 2 | 342 | 47.75 |
| Yaoguo Zheng | 3 | 102 | 7.09 |