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Image Attribute Adaptation
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DOI：10.1109/TMM.2014.2306092.png)
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摘要
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Visual attributes can be considered as a middle-level semantic cue that bridges the gap between low-level image features and high-level object classes. Thus, attributes have the advantage of transcending specific semantic categories or describing objects across categories. Since attributes are often human-nameable and domain specific, much work constructs attribute annotations ad hoc or take them from an application-dependent ontology. To facilitate other applications with attributes, it is necessary to develop methods which can adapt a well-defined set of attributes to novel images. In this paper, we propose a framework for image attribute adaptation. The goal is to automatically adapt the knowledge of attributes from a well-defined auxiliary image set to a target image set, thus assisting in predicting appropriate attributes for target images. In the proposed framework, we use a non-linear mapping function corresponding to multiple base kernels to map each training images of both the auxiliary and the target sets to a Reproducing Kernel Hilbert Space (RKHS), where we reduce the mismatch of data distributions between auxiliary and target images. In order to make use of un-labeled images, we incorporate a semi-supervised learning process. We also introduce a robust loss function into our framework to remove the shared irrelevance and noise of training images. Experiments on two couples of auxiliary-target image sets demonstrate that the proposed framework has better performance of predicting attributes for target testing images, compared to three baselines and two state-of-the-art domain adaptation methods.
Keyword:
Image attributes
domain adaptation
transfer learning
semi-supervised learning
multiple kernel learning
robust multiple kernel regression
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