1. Learnable Descriptors for Visual Search.
- Author
-
Migliorati, Andrea, Fiandrotti, Attilio, Francini, Gianluca, and Leonardi, Riccardo
- Subjects
VISUAL perception ,CONVOLUTIONAL neural networks ,IMAGE registration ,HAMMING distance - Abstract
This work proposes LDVS, a learnable binary local descriptor devised for matching natural images within the MPEG CDVS framework. LDVS descriptors are learned so that they can be sign-quantized and compared using the Hamming distance. The underlying convolutional architecture enjoys a moderate parameters count for operations on mobile devices. Our experiments show that LDVS descriptors perform favorably over comparable learned binary descriptors at patch matching on two different datasets. A complete pair-wise image matching pipeline is then designed around LDVS descriptors, integrating them in the reference CDVS evaluation framework. Experiments show that LDVS descriptors outperform the compressed CDVS SIFT-like descriptors at pair-wise image matching over the challenging CDVS image dataset. [ABSTRACT FROM AUTHOR]
- Published
- 2021
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