Although great process has been achieved to accurately classify images, many methods only use labeled images while ignoring the large quantity of unlabeled images. To make use of unlabeled images, in this letter, we propose a novel transductive visual recognition method using the prediction consistency of multiple views (T-PCMV). Both labeled and unlabeled images are used in a unified framework. The predictions of unlabeled images are learned by linearly combining the discriminative information of multiple views. We ensure the smooth constraint that visually similar images should be predicted with similar labels. To learn the classifier, we jointly minimize the classification loss and the discrepancy of predicted labels. To evaluate the usefulness of the proposed method, we conduct transductive visual recognition experiments on four image datasets. Experimental results well demonstrate the effectiveness of the proposed T-PCMV method.

中文翻译：

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探索用于转换视觉识别的多视图预测一致性

尽管已经实现了对图像进行准确分类的巨大过程，但是许多方法仅使用标记的图像，而忽略了大量未标记的图像。为了利用未标记的图像，在这封信中，我们提出了一种使用多视图预测一致性（T-PCMV）的新颖的转导视觉识别方法。带标签的图像和未带标签的图像都在统一框架中使用。未标记图像的预测是通过线性组合多个视图的判别信息来学习的。我们确保了平滑的约束，即应该使用相似的标签预测视觉上相似的图像。要学习分类器，我们将分类损失和预测标签的差异最小化。为了评估该方法的有效性，我们对四个图像数据集进行了转导视觉识别实验。