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In real world, an image is usually associated with multiple labels which are characterized by different regions in the image. Thus image classification is naturally posed as both a multi-label learning and multi-instance learning problem. Different from existing research which has considered these two problems separately, we propose an integrated multi-(More)
Conventional graph-based semi-supervised learning methods predominantly focus on single label problem. However, it is more popular in real-world applications that an example is associated with multiple labels simultaneously. In this paper, we propose a novel graph-based learning framework in the setting of semi-supervised learning with multiple labels. This(More)
Most of the existing metric learning methods are accomplished by exploiting pairwise constraints over the labeled data and frequently suffer from the insufficiency of training examples. To learn a robust distance metric from few labeled examples, prior knowledge from unlabeled examples as well as the metrics previously derived from auxiliary data sets can(More)
  • Zengfu Wang
  • 18th International Conference on Pattern…
  • 2006
In order to accomplish subject-independent facial expression recognition task, a multiple Gabor features based facial expression recognition method is presented in this paper. Different channels of Gabor filters have different contributions on the facial expression recognition and reasonable combination of these features can improve the performance of a(More)
Query suggestion is an effective approach to bridge the <i>Intention Gap</i> between the users' search intents and queries. Most existing search engines are able to automatically suggest a list of textual query terms based on users' current query input, which can be called Textual Query Suggestion. This article proposes a new query suggestion scheme named(More)
In this study, a novel adaptive sparse representation (ASR) model is presented for simultaneous image fusion and denoising. As a powerful signal modeling technique, sparse representation (SR) has been successfully employed in many image processing applications such as denoising and fusion. In traditional SR-based applications, a highly redundant dictionary(More)
Conventional graph-based semi-supervised learning methods predominantly focus on single label problem. However, it is more popular in real-world applications that an example is associated with multiple labels simultaneously. In this paper, we propose a novel graph-based learning framework in the setting of semi-supervised learning with multi-label. The(More)