Self-Supervised Online Metric Learning With Low Rank Constraint for Scene Categorization
Self-Supervised Online Metric Learning With Low Rank Constraint for Scene Categorization
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DOI：
10.1109/tip.2013.2260168
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发表时间：
2013-04
影响因子：
10.6
通讯作者：
Yang Cong;Ji Liu;Junsong Yuan;Jiebo Luo
中科院分区：
文献类型：
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作者：
Yang Cong;Ji Liu;Junsong Yuan;Jiebo Luo
传统的视觉识别系统通常在预先提供所有训练数据的情况下，以浴模式训练图像分类器。然而，在许多实际应用中，一开始只有少量的训练样本可用，在在线识别过程中会有更多的训练样本陆续出现。由于图像数据的特征可能会随着时间的推移而变化，因此分类器逐渐适应新数据非常重要。本文提出了一种在线度量学习方法，通过自适应相似度度量来解决在线场景识别问题。给定一些标记数据，然后依次输入未见过的测试样本，学习相似性度量以最大化不同类别样本之间的距离余量。通过考虑低秩约束，我们的在线度量学习模型不仅可以提供与现有方法相比具有竞争力的性能，而且可以保证收敛性。还定义了双线性图来建模成对相似性，并且根据基于图的标签传播对未见过的样本进行标记，同时模型还可以使用更自信的新样本进行自我更新。利用在线学习的能力，我们的方法可以很好地处理大规模的流视频数据，并具有增量自更新的能力。我们对我们的模型进行了在线场景分类评估，并在各种基准数据集上进行了实验，并与最先进的方法进行了比较，证明了我们算法的有效性和效率。
Conventional visual recognition systems usually train an image classifier in a bath mode with all training data provided in advance. However, in many practical applications, only a small amount of training samples are available in the beginning and many more would come sequentially during online recognition. Because the image data characteristics could change over time, it is important for the classifier to adapt to the new data incrementally. In this paper, we present an online metric learning method to address the online scene recognition problem via adaptive similarity measurement. Given a number of labeled data followed by a sequential input of unseen testing samples, the similarity metric is learned to maximize the margin of the distance among different classes of samples. By considering the low rank constraint, our online metric learning model not only can provide competitive performance compared with the state-of-the-art methods, but also guarantees convergence. A bi-linear graph is also defined to model the pair-wise similarity, and an unseen sample is labeled depending on the graph-based label propagation, while the model can also self-update using the more confident new samples. With the ability of online learning, our methodology can well handle the large-scale streaming video data with the ability of incremental self-updating. We evaluate our model to online scene categorization and experiments on various benchmark datasets and comparisons with state-of-the-art methods demonstrate the effectiveness and efficiency of our algorithm.