Generating preference indices for image content

Patent metadata

Field	Value
Publication number	US-9607217-B2
Application number	US-201414579998-A
Country	US
Kind code	B2
Filing date	Dec 22, 2014
Priority date	Dec 22, 2014
Publication date	Mar 28, 2017
Grant date	Mar 28, 2017

Abstract

Briefly, embodiments of methods and/or systems of generating preference indices for contiguous portions of digital images are disclosed. For one embodiment, as an example, parameters of a neural network may be developed to generate object labels for digital images. The developed parameters may be transferred to a neural network utilized to generate signal sample value levels corresponding to preference indices for contiguous portions of digital images.

First claim

What is claimed is:

1. A method of generating preference indices for image content utilizing one or more special-purpose computing devices to operate as a neural network and as another neural network, to operate without further human intervention, in which the one or more special-purpose computing devices includes one or more processors and one or more memory devices, comprising:
accessing computer instructions from the one or more memory devices of the one or more special-purpose computing devices for execution on the one or more processors of the one or more special-purpose computing devices;
executing the accessed computer instructions utilizing the one or more computing devices; and
storing, in the one or more memory devices of the one or more special-purpose computing devices, any results of having executed the accessed computer instructions on the one or more processors of the one or more special-purpose computing devices, wherein
the computer instructions to be executed comprise instructions for generating a term-independent preference index for a contiguous portion of an image using the another neural network, the another neural network including neural network parameters developed for the neural network to identify one or more object labels for a captured image, the another neural network including at least one classifier, not present in the neural network, to receive signal sample values from one or more fully-connected layers of the another neural network and to generate signal sample values corresponding to the preference indices based, at least in part, on the at least one classifier.

2. The method of claim 1 , wherein the term-independent preference index comprises a sentiment index.

3. The method of claim 1 , wherein the term-independent preference index comprises an odd number of allowed value levels.

4. The method of claim 1 , wherein the another neural network comprises a convolutional neural network.

5. The method of claim 4 , wherein the convolutional neural network comprises a deep convolutional neural network.

6. The method of claim 1 , wherein the neural network parameters developed for the another neural network are transferred from the neural network.

7. The method of claim 1 , wherein the generating the term-independent preference index includes generating the term-independent preference index via classification of signal samples generated by one or more convolutional layers of the another neural network.

8. The method of claim 7 , wherein the classification comprises classification via a machine learning process.

9. The method of claim 8 , wherein the machine learning process comprises a support vector machine process.

10. The method of claim 7 , wherein the classification comprises classification via a regression process.

11. An apparatus, comprising:
one or more special-purpose computing devices to operate as a neural network and as another neural network, the one or more special-purpose computing devices including one or more processors and one or more memory devices, to execute computer instructions on the one or more processors, without further human intervention, the computer instructions to be executed having been accessed from the one or more memory devices for execution on the one or more processors, the one or more special-purpose computing devices to store in the one or more memory devices any results to be generated from the execution of the computer instructions on the one or more processors;
the computer instructions to be executed comprising instructions for execution of generating preference indices for image content, wherein
the computer instructions to be executed by the one or more special-purpose computing devices further to comprise instructions to:
generate a term-independent preference index for a contiguous portion of an image using the another neural network, the another neural network to access neural network parameters developed for the neural network to identify one or more object labels for a captured image, the another neural network including at least one classifier, not present in the neural network, to receive signal sample values from one or more fully-connected layers of the another neural network and to generate signal sample values corresponding to the preference indices based, at least in part, on the at least one classifier.

12. The apparatus of claim 11 , the term-independent preference index is to comprise a sentiment index.

13. The apparatus of claim 11 , wherein the term-independent preference index is to comprise an odd number of allowed value levels.

14. The apparatus of claim 11 , wherein the another neural network is to comprise a deep convolutional neural network.

15. The apparatus of claim 11 , wherein the term-independent preference index is to be generated via classification of signal samples generated by one or more convolutional layers of the another neural network.

16. The apparatus of claim 11 , wherein the term-independent preference index is to be generated via classification of signal samples generated by one or more convolutional layers of the another neural network via a machine learning process.

17. An apparatus to generate a term-independent preference index for image content utilizing one or more special-purpose computing devices configured as a neural network and as another neural network, to operate without further human intervention, in which the one or more special-purpose computing devices includes one or more processors and one or more memory devices, comprising:
means for accessing computer instructions from the one or more memory devices of the one or more special-purpose computing devices for execution on the one or more processors of the one or more special-purpose computing devices;
means for executing the accessed computer instructions utilizing the one or more computing devices;
means for storing, in the one or more memory devices of the one or more special-purpose computing devices, any results of having executed the accessed computer instructions on the one or more processors of the one or more special-purpose computing devices; and
means for utilizing developed for the neural network, the neural network to identify one or more object labels for a captured image, to generate term-independent preference indices using the another neural network, the another neural network including at least one classifier, not present in the neural network, to receive signal sample values from one or more fully-connected layers of the another neural network and to generate signal sample values corresponding to the preference indices based, at least in part, on the at least one classifier.

18. The apparatus of claim 17 , further comprising means for classifying signal samples generated by one or more convolutional layers of the neural network.

19. The apparatus of claim 18 , wherein the means for classifying the signal samples comprises means for classifying via a machine learning process.

20. The apparatus of claim 17 , further comprising means for generating an odd number of allowed value levels.

Assignees

• Yahoo Inc

Inventors

• Cetintas Suleyman
• Lee Kuang-Chih
• Li Jia

Classifications

• G06V10/765

  using rules for classification or partitioning the feature space · CPC title

• G06V10/75

  Organisation of the matching processes, e.g. simultaneous or sequential comparisons of image or video features; Coarse-fine approaches, e.g. multi-scale approaches; using context analysis; Selection of dictionaries · CPC title

• G06V10/764

  using classification, e.g. of video objects · CPC title

• G06F18/24

  Classification techniques · CPC title

• G06V30/413 Primary

  Classification of content, e.g. text, photographs or tables · CPC title