Structured Visual Feature Learning for Classification via Supervised Probabilistic Tensor Factorization

• Published in: IEEE transactions on multimedia Vol. 17; no. 5; pp. 660 - 673
• Main Authors: Tan, Xu, Wu, Fei, Li, Xi, Tang, Siliang, Lu, Weiming, Zhuang, Yueting
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.05.2015; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Classification; Color; Equations; Factorization; Learning; Mathematical analysis; Mathematical model; Maximum entropy discrimination (MED); Multimedia; multimedia classification; Multimedia communication; Optimization; Preserves; Probabilistic logic; Representations; Streaming media; structural visual feature learning; supervised probabilistic tensor factorization; Tensile stress; Tensors; Visual
• ISSN: 1520-9210; 1941-0077
• DOI: 10.1109/TMM.2015.2410135

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 ε-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.