Data fusion through cross-modality metric learning using similarity-sensitive hashing

conference paper Data fusion through cross-modality metric learning using similarity-sensitive hashing published yes Michael M. Bronstein author Alexander Bronstein author 58f3726e-7cba-11ef-ad8b-e6e8cb3904e60000-0001-9699-8730 Fabrice Michel author Nikos Paragios author 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Visual understanding is often based on measuring similarity between observations. Learning similarities specific to a certain perception task from a set of examples has been shown advantageous in various computer vision and pattern recognition problems. In many important applications, the data that one needs to compare come from different representations or modalities, and the similarity between such data operates on objects that may have different and often incommensurable structure and dimensionality. In this paper, we propose a framework for supervised similarity learning based on embedding the input data from two arbitrary spaces into the Hamming space. The mapping is expressed as a binary classification problem with positive and negative examples, and can be efficiently learned using boosting algorithms. The utility and efficiency of such a generic approach is demonstrated on several challenging applications including cross-representation shape retrieval and alignment of multi-modal medical images. IEEE2010San Francisco, CA, USA eng 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition 1063-6919 10.1109/cvpr.2010.5539928 3594 - 3601 yes Bronstein MM, Bronstein AM, Michel F, Paragios N. Data fusion through cross-modality metric learning using similarity-sensitive hashing. In: 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. IEEE; 2010:3594-3601. doi:<a href="https://doi.org/10.1109/cvpr.2010.5539928">10.1109/cvpr.2010.5539928</a> Bronstein MM, Bronstein AM, Michel F, Paragios N. 2010. Data fusion through cross-modality metric learning using similarity-sensitive hashing. 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 3594–3601. Bronstein, Michael M., et al. “Data Fusion through Cross-Modality Metric Learning Using Similarity-Sensitive Hashing.” 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, IEEE, 2010, pp. 3594–601, doi:<a href="https://doi.org/10.1109/cvpr.2010.5539928">10.1109/cvpr.2010.5539928</a>. M. M. Bronstein, A. M. Bronstein, F. Michel, and N. Paragios, “Data fusion through cross-modality metric learning using similarity-sensitive hashing,” in 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Francisco, CA, USA, 2010, pp. 3594–3601. Bronstein, M. M., Bronstein, A. M., Michel, F., & Paragios, N. (2010). Data fusion through cross-modality metric learning using similarity-sensitive hashing. In 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (pp. 3594–3601). San Francisco, CA, USA: IEEE. <a href="https://doi.org/10.1109/cvpr.2010.5539928">https://doi.org/10.1109/cvpr.2010.5539928</a> M.M. Bronstein, A.M. Bronstein, F. Michel, N. Paragios, in:, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, IEEE, 2010, pp. 3594–3601. Bronstein, Michael M., Alex M. Bronstein, Fabrice Michel, and Nikos Paragios. “Data Fusion through Cross-Modality Metric Learning Using Similarity-Sensitive Hashing.” In 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 3594–3601. IEEE, 2010. <a href="https://doi.org/10.1109/cvpr.2010.5539928">https://doi.org/10.1109/cvpr.2010.5539928</a>. 183762024-10-15T11:20:54Z2024-12-02T14:00:22Z