Image retrieval method based on variable-length deep hash learning
US10776685B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | May 25, 2018 |
| Grant date | Sep 15, 2020 |
| Priority date | — |
| Expiry date | Nov 2, 2038 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06V30/274
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
This invention is an image retrieval method based on bit-scalable deep hashing learning. According to the method, the training images is used to generate a batch of image triples, wherein each of the triples contains two images with the same label and one image with a different label. The purpose of model training is to maximize a margin between matched image pairs and unmatched image pairs in the Hamming space. The deep convolutional neural network is utilized to train the model in an end-to-end fasion, where discriminative images features and has functions are simultaneously optimized. Furthermore, each bit of the hashing codes is unequally weighted so that we can manipulate the code length by truncating the insignificant bits. It is also shown that the generated bit-scalable hashing codes well preserve the discriminative powers with sorter code lengths.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.