We present a novel descriptor, called deep self-correlation (DSC), designed for establishing dense correspondences between images taken under different imaging modalities, such as different spectral ranges or lighting conditions. Motivated by local self-similarity (LSS), we formulate a novel descriptor by leveraging LSS in a deep architecture, leading to better discriminative power and greater robustness to non-rigid image deformations than state-of-the-art descriptors. The DSC first computes self-correlation surfaces over a local support window for randomly sampled patches, and then builds hierarchical self-correlation surfaces by performing an average pooling within a deep architecture. Finally, the feature responses on the self-correlation surfaces are encoded through a spatial pyramid pooling in a circular configuration. In contrast to convolutional neural networks (CNNs) based descriptors, the DSC is trainingfree, is robust to cross-modal imaging, and can be densely computed in an efficient manner that significantly reduces computational redundancy. The state-of-the-art performance of DSC on challenging cases of cross-modal image pairs is demonstrated through extensive experiments.
|Title of host publication||Computer Vision - 14th European Conference, ECCV 2016, Proceedings|
|Editors||Bastian Leibe, Jiri Matas, Nicu Sebe, Max Welling|
|Number of pages||17|
|Publication status||Published - 2016|
|Event||14th European Conference on Computer Vision, ECCV 2016 - Amsterdam, Netherlands|
Duration: 2016 Oct 8 → 2016 Oct 16
|Name||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Conference||14th European Conference on Computer Vision, ECCV 2016|
|Period||16/10/8 → 16/10/16|
Bibliographical notePublisher Copyright:
© Springer International Publishing AG 2016.
All Science Journal Classification (ASJC) codes
- Theoretical Computer Science
- Computer Science(all)