Approximate Log-Hilbert-Schmidt Distances between Covariance Operators for Image Classification
By Ha Quang Minh, Marco San-Biagio, Loris Bazzani, Vittorio Murino. 2016
23 citations recorded by the source corpus.
This paper presents a novel framework for visual object recognition using infinite-dimensional covariance operators of input features, in the paradigm of kernel methods on infinite-dimensional Riemannian manifolds. Our formulation provides a rich representation of image features by exploiting their non-linear correlations, using the power of kernel methods and Riemannian geometry. Theoretically, we provide an approximate formulation for the Log-Hilbert-Schmidt distance between covariance operators that is efficient to compute and scalable to large datasets. Empirically, we apply our framework to the task of image classification on eight different, challenging datasets. In almost all cases, the results obtained outperform other state of the art methods, demonstrating the competitiveness and potential of our framework.
Subfields: Computer Vision and Pattern Recognition
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In Computer Vision and Pattern Recognition
- Pietro Morerio
- Jacopo Cavazza
- Vittorio Murino
- Xiaogang Wang
- Yi Yang
- Liang Zheng
- Shaogang Gong
- Wanli Ouyang
Citations in the corpus
- When Kernel Methods Meet Feature Learning: Log-Covariance Network for Action Recognition From Skeletal Data (2017, methodology): “In addition to the remarkable property of being invariant with respect to sequence length T , the covariance representation was proved to be an effective tool for action classification [16, 11, 37, 10, 3].”