Abstract Statistical models have been widely adopted for image set classification owing to their capacity in characterizing the data distribution more flexibly and faithfully. However, these methods typically suffer from the problem that the query image set has weak statistical correlations with the training sets, which leads to larger fluctuations in performance. To address this problem, we propose a semi-supervised fuzzy discriminative learning framework based on Log-Euclidean multivariate Gaussians descriptor to facilitate more robust image set classification. Specifically, by using the semi-supervised setting which definitely has access to the labeled training data and the available unlabeled testing data, we adopt manifold distance metric to construct a “fully trusted” graph and derive two new data dependent probabilistic kernels to strongly reflect the underlying connection relationships between the training and query Gaussian manifold components. The resulted kernel representations are eventually integrated into a kernel fuzzy discriminant framework to enhance the compactness of intra-class Gaussian components and enlarge the margin for inter-class Gaussian components. Thus, more discriminating power of our learning machine is obtained for the classification of the query image set. Extensive experiments on several datasets well demonstrate the effectiveness of the proposed method compared with other image set algorithms.

中文翻译：

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基于统计分析的图像集分类半监督学习框架

摘要 统计模型因其能够更灵活、更忠实地表征数据分布的能力而被广泛用于图像集分类。然而，这些方法通常存在查询图像集与训练集的统计相关性较弱的问题，导致性能波动较大。为了解决这个问题，我们提出了一种基于 Log-Euclidean 多元高斯描述符的半监督模糊判别学习框架，以促进更稳健的图像集分类。具体来说，通过使用绝对可以访问标记训练数据和可用的未标记测试数据的半监督设置，我们采用流形距离度量来构建“完全可信”图，并推导出两个新的数据相关概率内核，以强烈反映训练和查询高斯流形组件之间的潜在连接关系。得到的核表示最终被集成到核模糊判别框架中，以增强类内高斯分量的紧凑性并扩大类间高斯分量的裕度。因此，对于查询图像集的分类，我们的学习机获得了更多的判别能力。与其他图像集算法相比，在几个数据集上的大量实验很好地证明了所提出方法的有效性。得到的核表示最终被集成到核模糊判别框架中，以增强类内高斯分量的紧凑性并扩大类间高斯分量的裕度。因此，对于查询图像集的分类，我们的学习机获得了更多的判别能力。与其他图像集算法相比，在几个数据集上的大量实验很好地证明了所提出方法的有效性。得到的核表示最终被集成到核模糊判别框架中，以增强类内高斯分量的紧凑性并扩大类间高斯分量的裕度。因此，对于查询图像集的分类，我们的学习机获得了更多的判别能力。与其他图像集算法相比，在几个数据集上的大量实验很好地证明了所提出方法的有效性。