Flat fine-grained image classification with progressive precision
US12322168B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Jan 25, 2022 |
| Grant date | Jun 3, 2025 |
| Priority date | — |
| Expiry date | Jan 13, 2044 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06V10/776
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Progressive precision image classifier and method of training include storing a dataset of labeled images, training a neural network to generate a classification vector comprising a plurality of confidence values, each confidence value corresponding to a classification, validating the trained neural network, calculating fine-grained confidence thresholds for each classification, wherein each classification represents a leaf-level classification in a hierarchical classification structure, and calculating coarse-level confidence thresholds for at least one parent class in the hierarchical classification structure, wherein each parent class defines a group of at least one leaf-level classification. Each label in the training data identifies a leaf-level classification in the hierarchical classification structure, and the classification vector includes a 1×N vector of confidence values, where N represents a number of leaf-level classifications output by the trained neural network. The neural network may be implemented as a convolution neural network with a single output head.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.