Machine learning assistant for image analysis
Systems, methods, and non-transitory computer readable media are provided for labeling depictions of objects within images. An image may be obtained. The image may include a depiction of an object. A user's marking of a set of dots within the image may be received. The set of dots may include one or more dots. The set of dots may be positioned within or near the depiction of the object. The depiction of the object within the image may be labeled based on the set of dots.
1. A system comprising:
one or more processors;
memory storing instructions that, when executed by the one or more processors, cause the system to perform:
obtaining an image, the image including a depiction of one or more objects;
receiving a marking of a set of pixel representations within the image, the set of pixel representations being positioned within or near the one or more objects, wherein the marking comprises a first portion associated with a first source and a second portion associated with a second source, and the first portion and the second portion are weighed differently based on previous accuracy or confidence levels corresponding to the first source and the second source; and
labeling a first object within the image based on a type of pixel representation corresponding to the first object.
2. The system of claim 1 , wherein:
the set of pixel representations comprise a first type of representation and a second type of representation,
the first type of representation indicates a first type or first category of the first object and the second type of representation indicates a second type or second category of a second object,
and the labeling of the first object is based on a first type or a first category.
3. The system of claim 2 , wherein the labelling is biased to respective centers of the first type of pixel representation and the second type of pixel representation.
4. The system of claim 2 , wherein the instructions further cause the system to perform:
extracting information from one or more databases associated with the first type of pixel representation and the second type of pixel representation; and
labelling the first object and the second object according to the extracted information.
5. The system of claim 4 , wherein the extracted information comprises geographic coordinates or characteristics of the first object and the second object.
6. The system of claim 1 , wherein the instructions further cause the system to perform:
determining and outputting a quality of the image, wherein the determination of the quality of the image compensates for a time of day and a season in which the image was obtained.
7. The system of claim 6 , wherein the quality of the image is determined based on a resolution and a blurriness of the image.
8. A method being implemented by a computing system including one or more physical processors and storage media storing machine-readable instructions, the method comprising:
obtaining an image, the image including a depiction of one or more objects;
receiving a marking of a set of pixel representations within the image, the set of pixel representations being positioned within or near the one or more objects, wherein the marking comprises a first portion associated with a first source and a second portion associated with a second source, and the first portion and the second portion are weighed differently based on previous accuracy or confidence levels corresponding to the first source and the second source; and
labeling a first object within the image based on a type of pixel representation corresponding to the first object.
9. The method of claim 8 , wherein:
the set of pixel representations comprise a first type of representation and a second type of representation,
the first type of representation indicates a first type or first category of the first object and the second type of representation indicates a second type or second category of a second object, and
the labeling of the first object is based on a first type or a first category.
10. The method of claim 9 , wherein the labelling is biased to respective centers of the first type of pixel representation and the second type of pixel representation.
11. The method of claim 9 , further comprising:
extracting information from one or more databases associated with the first type of pixel representation and the second type of pixel representation; and
labelling the first object and the second object according to the extracted information.
12. The method of claim 11 , wherein the extracted information comprises geographic coordinates or characteristics of the first object and the second object.
13. The method of claim 8 , further comprising:
determining and outputting a quality of the image, wherein the determination of the quality of the image compensates for a time of day and a season in which the image was obtained.
14. The method of claim 13 , wherein the quality of the image is determined based on a resolution and a blurriness of the image.
15. A non-transitory computer readable medium comprising instructions that, when executed, cause one or more processors to perform:
obtaining an image, the image including a depiction of one or more objects;
receiving a marking of a set of pixel representations within the image, the set of pixel representations being positioned within or near the one or more objects, wherein the marking comprises a first portion associated with a first source and a second portion associated with a second source, and the first portion and the second portion are weighed differently based on previous accuracy or confidence levels corresponding to the first source and the second source; and
labeling a first object within the image based on a type of pixel representation corresponding to the first object.
16. The non-transitory computer readable medium of claim 15 , wherein:
the set of pixel representations comprise a first type of representation and a second type of representation,
the first type of representation indicates a first type or first category of the first object and the second type of representation indicates a second type or second category of a second object,
and the labeling of the first object is based on a first type or a first category.
17. The non-transitory computer readable medium of claim 16 , wherein the labelling is biased to respective centers of the first type of pixel representation and the second type of pixel representation.
18. The non-transitory computer readable medium of claim 16 , wherein the instructions further cause the system to perform:
extracting information from one or more databases associated with the first type of pixel representation and the second type of pixel representation; and
labelling the first object and the second object according to the extracted information.