Discriminative Structure Learning for Semantic Concept Detection With Graph Embedding

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.