Entity recognition from an image
Aspects of the current disclosure include systems and methods for identifying an entity in a query image by comparing the query image with digital images in a database. In one or more embodiments, a query feature may be extracted from the query image and a set of candidate features may be extracted from a set of images in the database. In one or more embodiments, the distances between the query feature and the candidate features are calculated. A feature, which includes a set of shortest distances among the calculated distances and a distribution of the set of shortest distances, may be generated. In one or more embodiments, the feature is input to a trained model to determine whether the entity in the query image is the same entity associated with one of the set of shortest distances.
1. A method comprising:
generating a query feature for a query image, the query feature including one or more features about an entity extracted from the query image;
determining similarity measures between the query feature and each of a set of candidate features that correspond to a set of candidate images;
selecting a set of closest similarity measures among the determined similarity measures;
generating a feature comprising the set of closest similarity measures and a distribution of the set of closest similarity measures;
obtaining a match probability value using the feature and a trained model; and
responsive to the match probability value exceeding a threshold value, determining the entity extracted from the query image is the same entity associated with an image corresponding to the candidate feature that has a closest similarity measure among the set of closest similarity measures.
2. The method of claim 1 , further comprising:
generating an original feature that includes one or more features extracted from the query image; and
converting the original feature into the query feature by reducing dimensionality of the original feature.
3. The method of claim 2 , further comprising:
inputting the original feature to a principal component analysis map to obtain the query feature.
4. The method of claim 1 , further comprising:
reducing, prior to determining the similarity measures, a search space comprising a plurality of features to obtain the set of candidate features.
5. The method of claim 4 , further comprising:
using an index parameter associated with the query image, selecting the set of candidate features among the plurality of features.
6. The method of claim 4 , further comprising:
using one or more k-d trees, selecting the set of candidate images among the plurality of features.
7. The method of claim 1 , further comprising:
training a model using a set of features that correspond to a set of candidate images associated with entities having known identities.
8. A system comprising:
one or more processors; and
at least one memory including instructions that, when executed by the one or more processors, cause the one or more processors to perform operations comprising:
generating a query feature for a query image, the query feature including features about an entity extracted from the query image;
determining similarity measures between the query feature and each of a set of candidate features that correspond to a set of candidate images;
selecting a set of closest similarity measures among the determined similarity measures;
generating a feature comprising a set of closest similarity measures and a distribution of the set of closest similarity measures;
obtaining a match probability value using the feature and a trained model; and
responsive to the match probability value exceeding a threshold value, determining the entity associated extracted from the query image is the same entity associated with an image corresponding to the candidate feature that has a closest similarity measure among the set of closest similarity measures.
9. The system of claim 8 , wherein the operations further comprise:
generating an original feature that includes one or more features extractedthe query image; and
converting the original feature into the query feature by reducing dimensionality of the original feature.
10. The system of claim 9 , wherein the operations further comprise:
inputting the original feature to a principal component analysis map to obtain the query feature.
11. The system of claim 8 , wherein the operations further comprise:
reducing a search space comprising a plurality of features to obtain the set of candidate features.
12. The system of claim 11 , wherein the operations further comprise:
using an index parameter associated with the query image, selecting the set of candidate features among the plurality of features.
13. The system of claim 8 , wherein the operations further comprise:
training a model using a set of features that correspond to a set of candidate images associated with entities having known identities.
14. A non-transitory computer-readable medium or media comprising one or more sets of instructions, which causes one or more processors to perform operations comprising:
generating a query feature for a query image, the query feature including features about an entity extracted from the query image;
determining similarity measures between the query feature and each of a set of candidate features that correspond to a set of candidate images;
selecting a set of closest similarity measures among the determined similarity measures;
generating a feature comprising the set of closest similarity measures and a distribution of the set of closest similarity measures;
obtaining a match probability value using the feature and a trained model; and
responsive to the match probability value exceeding a threshold value, determining the entity extracted from the query image is the same entity associated with an image corresponding to the candidate feature that has a closest similarity measure among the set of closest similarity measures.
15. The non-transitory computer-readable medium or media of claim 14 , wherein the operations further comprise:
generating an original feature that includes one or more features extracted from the query image; and
converting the original feature into the query feature by reducing dimensionality of the original feature.
16. The non-transitory computer-readable medium or media of claim 15 , wherein the operations further comprise:
inputting the original feature to a principal component analysis map to obtain the query feature.
17. The non-transitory computer-readable medium or media of claim 14 , wherein the operations further comprise:
reducing a search space comprising a plurality of features to obtain the set of candidate features.
18. The non-transitory computer-readable medium or media of claim 17 , wherein the operations further comprise:
using an index parameter associated with the query image, selecting the set of candidate features among the plurality of features.
19. The non-transitory computer-readable medium or media of claim 17 , wherein the operations further comprise:
using one or more k-d trees, selecting the set of candidate images among the plurality of features.
20. The non-transitory computer-readable medium or media of claim 14 , wherein the operations further comprise:
training a model using a set of features that correspond to a set of candidate images associated with entities having known identities.