Abstract
Large scale instance recognition is provided that can take advantage of channel-wise pooling. A received query image is processed to extract a set of features that can be used to generate a set of region proposals. The proposed regions of image data are processed using a trained classifier to classify the regions as object or non-object regions. Extracted features for the object regions are processed using feature correlation against extracted features for a set of object images, each representing a classified object. Matching tensors generated from the comparison are processed using a spatial verification network to determine match scores for the various object images with respect to a specific object region. The match scores are used to determine which objects, or types of objects, are represented in the query image. Information or content associated with the matching objects can be provided as part of a response.
Key facts
- Application
- 16/123,130Filed 6 Sep 2018
- Granted
- 24 Nov 2020
- Term ends
- Not estimatedThe source records don’t support an estimate.
Classification17 codes
- G06F 16/532
Querying: query formulation, e.g. graphical querying
- G06F 16/538
Querying: presentation of query results
- G06F 16/583
Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually: using metadata automatically…
- G06F 16/5854
Using metadata automatically derived from the content: using shape and object relationship
Family
No related US applications are recorded in the indexed filing history.