COMPUTER VISION AS A SERVICE

• US 20170091590A1
• Filed: 10/05/2016
• Published: 03/30/2017
• Est. Priority Date: 03/15/2013
• Status: Abandoned Application

• Current Assignee: SRI International, Inc.
• Original Assignee: SRI International, Inc.
• Inventors: SAWHNEY, Harpreet Singh, ELEDATH, Jayakrishan, ALI, Saad, MATEI, Bogdan C., Weiner, Steven S., Lv, Xutao, Shields, Timothy J.
• Application Number:
  US15/285,679
• Publication Number:
  US 20170091590A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06F 18/217 Validation; Performance evaluation; Active pattern learning techniques
• CPC Class Codes: G06F 18/23 Clustering techniques
• CPC Class Codes: G06F 18/40 Software arrangements specially adapted for pattern recognition, e.g. user interfaces or toolboxes therefor
• CPC Class Codes: G06V 10/945 User interactive design; Environments; Toolboxes
• CPC Class Codes: G06V 10/95 structured as a network, e.g. client-server architectures
• CPC Class Codes: G06V 10/96 Management of image or video recognition tasks

Abstract

A computer vision service includes technologies to, among other things, analyze computer vision or learning tasks requested by computer applications, select computer vision or learning algorithms to execute the requested tasks based on one or more performance capabilities of the computer vision or learning algorithms, perform the computer vision or learning tasks for the computer applications using the selected algorithms, and expose the results of performing the computer vision or learning tasks for use by the computer applications.

Claims

1-32. -32. (canceled)

33. A platform for providing machine learning algorithm services to user-oriented computer applications, the platform comprising:

a performance interface to evaluate a capability of each of a plurality of candidate machine learning algorithms to perform a machine learning task received from a computer application on digital content, the performance capability being determined at least in part by a characteristic of the digital content indicated by at least one application parameter of a plurality of application parameters determined by an application-algorithm interface, wherein identification of the plurality of candidate machine learning algorithms is based on the application parameters; and

an algorithm organization framework to organize the plurality of candidate machine learning algorithms according to a plurality of different levels of abstraction, wherein the platform is to select a level of abstraction based on the machine learning task,

wherein the platform is to select a machine learning algorithm from the plurality of candidate machine learning algorithms based on the evaluation performed by the performance interface.

35. The platform of claim 33, wherein the platform is to perform the machine learning task on the digital content by executing the selected machine learning algorithm using the one or more algorithm parameters.

36. The platform of claim 35, wherein the platform is to communicate a result of executing the selected machine learning algorithm to the computer application.

37. The platform of claim 33, further comprising a data organization service to index a plurality of reference data for use by the platform in executing the machine learning algorithms.

38. The platform of claim 37, wherein the data organization service creates the plurality of reference data by analyzing and indexing data from a plurality of databases and data stores.

39. The platform of claim 38, wherein the data organization service provides a database access interface to the plurality of reference data for the computer application.

40. The platform of claim 39, wherein the database access interface enables the user-oriented computer applications or the platform to poll retrieve stored data according to one or more criteria, selected from a group including a spatial or volumetric characteristic, an attribute-based characteristic, feature similarity and temporal access.

41. The platform of claim 38, wherein a reference data index automatically indexes visual data and metadata from structured, semi-structured and unstructured data stores.

42. The platform of claim 33, wherein the performance interface is to provide performance estimates of each of the plurality of candidate machine learning algorithms at multiple levels of performance characterization.

43. The platform of claim 38, wherein the multiple levels of performance characterization include a level that performs a projected performance characterization that quantifies each of the plurality of candidate machine learning algorithms'"'"' performance against known datasets.

44. The platform of claim 38, wherein the multiple levels of performance characterization include a level that performs rapid analysis of input without fully running each of the plurality of candidate machine learning.

45. The platform of claim 38, wherein the multiple levels of performance characterization include a level that determines each of the plurality of candidate machine learning algorithms'"'"' suitability for a particular task.

46. The platform of claim 38, wherein the multiple levels of performance characterization includes a level that performs a diagnostic characterization that characterizes the uncertainty of specific point solutions obtained by executing each of the plurality of candidate machine learning.

56. An Internet-based service comprising instructions embodied in one or more non-transitory machine accessible storage media, the instructions executable by one or more processors to perform the method of claim 43.

57. One or more non-transitory machine accessible storage media having embodied therein a plurality of instructions executable by a processor to perform the method of claim 43.

34. The platform of claim 34, further comprising an algorithm parameter mapping module to map the application parameters to one or more algorithm parameters to use with the selected machine learning algorithm to perform the machine learning task on the digital content.

47. A method for providing machine learning algorithm services to computer applications, the method comprising, with at least one computing device:

evaluating a capability of each of a plurality of candidate machine learning algorithms to perform a machine learning task on digital content, the performance capability being determined at least in part by a characteristic of the digital content indicated by at least one application parameter of a plurality of application parameters, wherein identification of the plurality of candidate machine learning algorithms is based on the application parameters;

organizing the plurality of candidate machine learning algorithms, using an algorithm organization framework, according to a plurality of different levels of abstraction,

selecting a level of abstraction based on the machine learning task;

selecting a machine learning algorithm from the plurality of candidate machine learning algorithms based on the evaluation of the capability of each of the plurality of candidate machine learning algorithms;

performing the machine learning task by executing the selected machine learning algorithm with the parameter; and

communicating a result of the executing of the machine learning algorithm to the computer application.

48. The method of claim 47, wherein the machine learning task comprises analyzing digital content, and the method further comprises determining the parameter based on an attribute of the digital content.

49. The method of claim 48, further comprising using the attribute of the digital content to determine a performance characteristic of the machine learning algorithm.

50. The method of claim 47, further comprising determining the performance characteristic of the machine learning algorithm by executing a content clustering technique based on the attribute of the digital content.

51. The method of claim 47, wherein the evaluation of the capability of each of a plurality of candidate machine learning algorithms is to perform estimates of each of the plurality of candidate machine learning algorithms at multiple levels of performance characterization.

52. The method of claim 51, wherein the multiple levels of performance characterization include a level that performs a projected performance characterization that quantifies each of the plurality of candidate machine learning algorithms'"'"' performance against known datasets

53. The method of claim 51, wherein the multiple levels of performance characterization include a level that performs rapid analysis of input without fully running each of the plurality of candidate machine learning.

54. The method of claim 51, wherein the multiple levels of performance characterization include a level that determines each of the plurality of candidate machine learning algorithms'"'"' suitability for a particular task.

55. The method of claim 51, wherein the multiple levels of performance characterization includes a level that performs a diagnostic characterization that characterizes the uncertainty of specific point solutions obtained by executing each of the plurality of candidate machine learning.