Label consistency for image analysis

Abstract

Systems and techniques are disclosed for labeling objects within an image. The objects may be labeled by selecting an option from a plurality of options such that each option is a potential label for the object. An option may have an option score associated with. Additionally, a relation score may be calculated for a first option and a second option corresponding to a second object in an image. The relation score may be based on a frequency, probability, or observance corresponding to the co-occurrence of text associated with the first option and the second option in a text corpus such as the World Wide Web. An option may be selected as a label for an object based on a global score calculated based at least on an option score and relation score associated with the option.

1 . (canceled) 
     
     
         2 . A computer implemented method comprising:
 receiving, by the computer, a first option for a first object in an image from a first plurality of options;   receiving, by the computer, a second option for the first object in the image from a first plurality of options;   receiving, by the computer, a control label for a second object in the image;   generating a first relation score between the first option and the control label based on a co-occurrence model;   generating, by the computer, a second relation score between the second option and the control label based on the co-occurrence model;   determining that the first relation score exceeds the second relation score; and   designating the first option as a label for the first object in the image.   
     
     
         3 . The method of  claim 2 , wherein the co-occurrence model is a machine learning model trained using a text corpus. 
     
     
         4 . The method of  claim 2 , wherein generating the first relation score further comprises, determining the probability that text associated with the first option and the control option co-occur within a text corpus. 
     
     
         5 . The method of  claim 2 , wherein generating the first relation score further comprises applying the co-occurrence model to the first option and the control option wherein the co-occurrence model is generated based at least on co-occurrence within a limit selected from the group consisting of: adjacent words or terms, a same sentence, a same paragraph, a same page, and a same document. 
     
     
         6 . The method of  claim 3 , wherein the text corpus is a portion of the World Wide Web. 
     
     
         7 . The method of  claim 2 , wherein the first option is generated using a label generator. 
     
     
         8 . The method of  claim 2 , further comprising:
 receiving a search query;   identifying the first image based on the label for the first object as a result for the query; and   providing the first image based on the identification.   
     
     
         9 . The method of  claim 2 , further comprising designating the first option as a label for the first object based on a regularization factor. 
     
     
         10 . A system comprising:
 a processor configured to:
 receive a first option for a first object in an image from a first plurality of options; 
 receive a second option for the first object in the image from a first plurality of options; 
 receive a control label for a second object in the image; 
 generate a first relation score between the first option and the control label based on a co-occurrence model; 
 generate, by the computer, a second relation score between the second option and the control label based on the co-occurrence model; 
 determine that the first relation score exceeds the second relation score; and 
 designate the first option as a label for the first object in the image. 
   
     
     
         11 . The system of  claim 10 , wherein the co-occurrence model is a machine learning model trained using a text corpus. 
     
     
         12 . The system of  claim 10 , wherein generating the first relation score further comprises, determining the probability that text associated with the first option and the control option co-occur within a text corpus. 
     
     
         13 . The system of  claim 10 , further configured to:
 receive a search query;   identify the first image based on the label for the first object as a result for the query; and   provide the first image based on the identification.   
     
     
         14 . A device comprising:
 a storage; and   a processor configured to:
 receive a first option for a first object in an image from a first plurality of options; 
 receive a second option for the first object in the image from a first plurality of options; 
 receive a control label for a second object in the image; 
 generate a first relation score between the first option and the control label based on a co-occurrence model; 
 generate, by the computer, a second relation score between the second option and the control label based on the co-occurrence model; 
 determine that the first relation score exceeds the second relation score; and 
 designate the first option as a label for the first object in the image. 
   
     
     
         15 . The device of  claim 14 , wherein the co-occurrence model is a machine learning model trained using a text corpus. 
     
     
         16 . The device of  claim 14 , wherein generating the first relation score further comprises, determining the probability that text associated with the first option and the control option co-occur within a text corpus. 
     
     
         17 . The device of  claim 14 , further configured to:
 receive a search query;   identify the first image based on the label for the first object as a result for the query; and   provide the first image based on the identification.