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Random Forest Classifier for Zero-Shot Learning Based on Relative Attribute.

Random Forest Classifier for Zero-Shot Learning Based on Relative Attribute. For the zero-shot image classification with relative attributes (RAs), the traditional method requires that not only all seen and unseen images obey Gaussian distribution, but also the classifications on testing samples are made by maximum likelihood estimation. We therefore propose a novel zero-shot image classifier called random forest based on relative attribute. First, based on the ordered and unordered pairs of images from the seen classes, the idea of ranking support vector machine is used to learn ranking functions for attributes. Then, according to the relative relationship between seen and unseen classes, the RA ranking-score model per attribute for each unseen image is built, where the appropriate seen classes are automatically selected to participate in the modeling process. In the third step, the random forest classifier is trained based on the RA ranking scores of attributes for all seen and unseen images. Finally, the class labels of testing images can be predicted via the trained RF. Experiments on Outdoor Scene Recognition, Pub Fig, and Shoes data sets show that our proposed method is superior to several state-of-the-art methods in terms of classification capability for zero-shot learning problems. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png IEEE transactions on neural networks and learning systems Pubmed

Random Forest Classifier for Zero-Shot Learning Based on Relative Attribute.

IEEE transactions on neural networks and learning systems , Volume 29 (5): 13 – Mar 19, 2019

Random Forest Classifier for Zero-Shot Learning Based on Relative Attribute.


Abstract

For the zero-shot image classification with relative attributes (RAs), the traditional method requires that not only all seen and unseen images obey Gaussian distribution, but also the classifications on testing samples are made by maximum likelihood estimation. We therefore propose a novel zero-shot image classifier called random forest based on relative attribute. First, based on the ordered and unordered pairs of images from the seen classes, the idea of ranking support vector machine is used to learn ranking functions for attributes. Then, according to the relative relationship between seen and unseen classes, the RA ranking-score model per attribute for each unseen image is built, where the appropriate seen classes are automatically selected to participate in the modeling process. In the third step, the random forest classifier is trained based on the RA ranking scores of attributes for all seen and unseen images. Finally, the class labels of testing images can be predicted via the trained RF. Experiments on Outdoor Scene Recognition, Pub Fig, and Shoes data sets show that our proposed method is superior to several state-of-the-art methods in terms of classification capability for zero-shot learning problems.

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ISSN
2162-237X
DOI
10.1109/TNNLS.2017.2677441
pmid
28333644

Abstract

For the zero-shot image classification with relative attributes (RAs), the traditional method requires that not only all seen and unseen images obey Gaussian distribution, but also the classifications on testing samples are made by maximum likelihood estimation. We therefore propose a novel zero-shot image classifier called random forest based on relative attribute. First, based on the ordered and unordered pairs of images from the seen classes, the idea of ranking support vector machine is used to learn ranking functions for attributes. Then, according to the relative relationship between seen and unseen classes, the RA ranking-score model per attribute for each unseen image is built, where the appropriate seen classes are automatically selected to participate in the modeling process. In the third step, the random forest classifier is trained based on the RA ranking scores of attributes for all seen and unseen images. Finally, the class labels of testing images can be predicted via the trained RF. Experiments on Outdoor Scene Recognition, Pub Fig, and Shoes data sets show that our proposed method is superior to several state-of-the-art methods in terms of classification capability for zero-shot learning problems.

Journal

IEEE transactions on neural networks and learning systemsPubmed

Published: Mar 19, 2019

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