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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)
In this paper, we propose a two-dimensional active learning scheme and show its application in image classification. Traditional active learning methods select samples only along the sample dimension. While this is the right strategy in binary classification, it is sub-optimal for multi-label classification. In multi-label classification, we argue that, for(More)
Automatically annotating concepts for video is a key to semantic-level video browsing, search and navigation. The research on this topic evolved through two paradigms. The first paradigm used binary classification to detect each individual concept in a concept set. It achieved only limited success, as it did not model the inherent correlation between(More)
In this article, we exploit the problem of annotating a large-scale image corpus by label propagation over noisily tagged web images. To annotate the images more accurately, we propose a novel <i>k</i>NN-sparse graph-based semi-supervised learning approach for harnessing the labeled and unlabeled data simultaneously. The sparse graph constructed by(More)
Learning-based video annotation is a promising approach to facilitating video retrieval and it can avoid the intensive labor costs of pure manual annotation. But it frequently encounters several difficulties, such as insufficiency of training data and the curse of dimensionality. In this paper, we propose a method named optimized multigraph-based(More)
Automatic video annotation is an important ingredient for semantic-level video browsing, search and navigation. Much attention has been paid to this topic in recent years. These researches have evolved through two paradigms. In the first paradigm, each concept is individually annotated by a pre-trained binary classifier. However, this method ignores the(More)
In this paper, we exploit the problem of inferring images' semantic concepts from community-contributed images and their associated noisy tags. To infer the concepts more accurately, we propose a novel sparse graph-based semi-supervised learning approach for harnessing the labeled and unlabeled data simultaneously. The sparse graph constructed by datum-wise(More)
The problem of community detection in social media has been widely studied in the social networking community in the context of the structure of the underlying graphs. Most community detection algorithms use the links between the nodes in order to determine the dense regions in the graph. These dense regions are the communities of social media in the graph.(More)
This paper proposes an efficient sparse metric learning algorithm in high dimensional space via an <i>l</i><sub>1</sub>-penalized log-determinant regularization. Compare to the most existing distance metric learning algorithms, the proposed algorithm exploits the sparsity nature underlying the intrinsic high dimensional feature space. This sparsity prior of(More)
In recent years, knowledge transfer algorithms have become one of most the active research areas in learning visual concepts. Most of the existing learning algorithms focuses on leveraging the knowledge transfer process which is specific to a given category. However, in many cases, such a process may not be very effective when a particular target category(More)