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Structured Visual Feature Learning for Classification via Supervised Probabilistic Tensor Factorization
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DOI：10.1109/TMM.2015.2410135.png)
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Abstract
En 中文
In this paper, structured visual feature learning aims at exploiting the intrinsic structural properties of mutually correlated multimedia collections (e.g., video frames or facial images) to learn a more effective feature representation for multimedia data classification. We pose structured visual feature learning as a problem of supervised tensor factorization (STF), which is capable of effectively learning multi-view visual features from structural tensorial multimedia data. In mathematics, STF is formulated as a joint optimization framework of probabilistic inference and epsilon-insensitive support vector regression. As a result, the feature representation obtained by STF not only preserves the intrinsic multi-view structural information on tensorial multimedia data, but also includes the discriminative information derived from the max-margin learning process. Using the learned discriminative visual features, we conduct a set of multimedia classification experiments on several challenging datasets, including images and videos, which demonstrate the effectiveness of our method.
Keywords:
Maximum entropy discrimination (MED)
multimedia classification
structural visual feature learning
supervised probabilistic tensor factorization
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