19 July 2013 Adaptive covariance matrix for object region representation
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Proceedings Volume 8878, Fifth International Conference on Digital Image Processing (ICDIP 2013); 887848 (2013) https://doi.org/10.1117/12.2030558
Event: Fifth International Conference on Digital Image Processing, 2013, Beijing, China
Region covariance descriptor has been employed in many computer vision applications such as texture classification, object detection and object tracking. It provides a natural way of fusing multiple features based on a set of pixels of a given region. However, the discriminative capacity of covariance descriptor can vary dramatically regarding different combination of feature sets that are fused and thus gives rise to the problem of discriminative feature selection when given a specific application. In this work, we propose a PCA-based feature selection approach in the construction procedure of the covariance descriptor. We show that covariance descriptor computed in a minutely-learned subspace can be adaptive to a specific target and thus results in a more compact and potentially more discriminative descriptor. Comparing experiments on real world video sequences demonstrate superior representational ability of the proposed method with respect to the conventional region covariance descriptor.
© (2013) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Lei Qin, Lei Qin, Hichem Snoussi, Hichem Snoussi, Fahed Abdallah, Fahed Abdallah, "Adaptive covariance matrix for object region representation", Proc. SPIE 8878, Fifth International Conference on Digital Image Processing (ICDIP 2013), 887848 (19 July 2013); doi: 10.1117/12.2030558; https://doi.org/10.1117/12.2030558

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