Image analysis and identification using machine learning with output estimation

Abstract

The present disclosure relates to systems and methods for generating real-time quotes using machine learning algorithms. The system may include a processor in communication with a client device, and a storage medium storing instructions that, when executed, cause the processor to perform operations including: receiving an image of a vehicle from the client device, extracting one or more features from the image, based on the extracted features and using a machine learning algorithm, identifying one or more attributes of the vehicle, based on the identified attributes of the vehicle, determining a make and a model of the vehicle, obtaining comparison information based at least in part on the determined make and model, estimating a quote for the vehicle based on the comparison information; and transmitting the estimated quote for display on the client device.

1 - 20 . (canceled) 
     
     
         21 . A computer implemented method for comparing images, the method comprising:
 obtaining a first image from a database;   extracting machine learning features from the first image;   obtaining a second image from a client device;   extracting machine learning features from the second image;   calculating a comparison metric based on the extracted machine learning features of the first image and the extracted machine learning features of the second image;   determining whether the comparison metric satisfies a matching criterion; and   identifying, based on the matching criterion, that the second image is similar to the first image.   
     
     
         22 . The method of  claim 21 , wherein the extracted machine learning features includes features extracted from an image from a database and an image from a mobile device. 
     
     
         23 . The method of  claim 21 , wherein calculating a comparison metric comprises inputting the machine learning features extracted from the database image and the machine learning features extracted from the mobile device into a classifier. 
     
     
         24 . The method of  claim 21 , wherein the comparison metric includes one or more instances values configured to:
 increase with increasing similarity among one or more images; or   decrease with increasing similarity among one or more images.   
     
     
         25 . The method of  claim 21 , wherein determining whether the comparison metric satisfies a matching criterion includes determining whether a value of the comparison metric exceeds a threshold value. 
     
     
         26 . The method of  claim 21 , wherein the first image includes metadata providing location information associated with when the first image was obtained, the metadata being embedded in the first image by the client device. 
     
     
         27 . The method of  claim 21 , wherein the extracted machine learning model features are extracted by inputting the first image or a version of the first image into a first convolutional neural network. 
     
     
         28 . The method of  claim 27 , wherein the first convolutional neural network is configured to output the extracted machine learning features. 
     
     
         29 . The method of  claim 27 , wherein the extracted machine learning model features are extracted by inputting the second image or a version of the second image into a second convolutional neural network. 
     
     
         30 . The method of  claim 29 , wherein the second convolutional neural network is configured to output indications of one or more vehicle attributes. 
     
     
         31 . An image processing system including a vehicle using machine learning, comprising:
 at least one processor; and   at least one storage medium containing instructions that, when executed by the at least one processor, cause the image processing system to perform operations comprising:
 obtaining a first image from a database; 
 extracting machine learning features from the first image; 
 obtaining a second image from a client device; 
 extracting machine learning features from the second image; 
 calculating a comparison metric based on the extracted machine learning features of the first image and the extracted machine learning features of the second image; 
 determining whether the comparison metric satisfies a matching criterion; and 
 identifying, based on the matching criterion, that the second image is similar to the first image. 
   
     
     
         32 . The system of  claim 31 , wherein the extracted machine learning features includes features extracted from an image from a database and an image from a mobile device. 
     
     
         33 . The system of  claim 31 , wherein calculating a comparison metric comprises inputting the machine learning features extracted from the database image and the machine learning features extracted from the mobile device into a classifier. 
     
     
         34 . The system of  claim 31 , wherein the comparison metric includes one or more instances values configured to:
 increase with increasing similarity among one or more images; and   decrease with increasing similarity among one or more images.   
     
     
         35 . The system of  claim 31 , wherein determining whether the comparison metric satisfies a matching criterion includes determining whether a value of the comparison metric exceeds a threshold value. 
     
     
         36 . The system of  claim 31 , wherein the first image includes metadata providing location information associated with when the first image was obtained, the metadata being embedded in the first image by the client device. 
     
     
         37 . The system of  claim 31 , wherein the extracted machine learning model features are extracted by inputting the first image or a version of the first image into a first convolutional neural network. 
     
     
         38 . The system of  claim 37 , wherein the first convolutional neural network is configured to output the extracted machine learning features. 
     
     
         39 . The system of  claim 37 , wherein the extracted machine learning model features are extracted by inputting the second image or a version of the second image into a second convolutional neural network. 
     
     
         40 . The system of  claim 37 , wherein the second convolutional neural network is configured to output indications of one or more vehicle attributes.