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A semi-supervised domain adaptation assembling approach for image classification

机译：用于图像分类的半监督域自适应组装方法

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摘要

Automatic annotation of images is one of the fundamental problems in computer vision applications. With the increasing amount of freely available images, it is quite possible that the training data used to learn a classifier has different distribution from the data which is used for testing. This results in degradation of the classifier performance and highlights the problem known as domain adaptation. Framework for domain adaptation typically requires a classification model which can utilize several classifiers by combining their results to get the desired accuracy. This work proposes depth-based and iterative depth-based fusion methods which are basically rank-based fusion methods and utilize rank of the predicted labels from different classifiers. Two frameworks are also proposed for domain adaptation. The first framework uses traditional machine learning algorithms, while the other works with metric learning as well as transfer learning algorithm. Motivated from ImageCLEF's 2014 domain adaptation task, these frameworks with the proposed fusion methods are validated and verified by conducting experiments on the images from five domains having varied distributions. Bing, Caltech, ImageNet, and PASCAL are used as source domains and the target domain is SUN. Twelve object categories are chosen from these domains. The experimental results show the performance improvement not only over the baseline system, but also over the winner of the ImageCLEF's 2014 domain adaptation challenge.
机译：图像的自动注释是计算机视觉应用程序中的基本问题之一。随着免费提供的图像数量的增加，用于学习分类器的训练数据很有可能与用于测试的数据具有不同的分布。这导致分类器性能下降，并突出了称为域自适应的问题。域自适应框架通常需要一个分类模型，该模型可以通过组合多个分类器的结果来使用多个分类器，以获得所需的准确性。这项工作提出了基于深度和基于迭代深度的融合方法，它们基本上是基于等级的融合方法，并利用来自不同分类器的预测标签的等级。还提出了两个用于领域适应的框架。第一个框架使用传统的机器学习算法，而其他框架则与度量学习以及转移学习算法一起使用。从ImageCLEF的2014年领域适应任务的动机出发，通过对来自五个具有不同分布的域的图像进行实验，验证并验证了采用拟议融合方法的这些框架。 Bing，Caltech，ImageNet和PASCAL用作源域，而目标域是SUN。从这些域中选择十二个对象类别。实验结果表明，性能不仅在基线系统上有所提高，而且在ImageCLEF的2014年领域自适应挑战赛的获胜者上也有所提高。

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