Optimal cropping of digital image based on professionalism score of subject

1. A computerized method for automatically cropping a digital image to improve perceived professionalism of a subject of the image, the method comprising:

utilizing a machine learning algorithm to generate a professionalism score for the digital image, wherein the professionalism score indicates a perceived professionalism of a human depicted in the digital image, the utilizing a machine learning algorithm comprising:

a training mode where a plurality of sample images with labeled professionalism scores are used to train a classification function in a model that produces as professionalism score as output,

an analysis mode where the model is used to generate a professionalism score for the digital image; and

using the professionalism score for the digital image as an input to a discrete variable optimization algorithm to determine an optimum cropped version of the digital image from a plurality of possible cropped versions of the digital image using the classification function.

2. The method of claim 1 , wherein the classification function contains one or more weights, and wherein the training of the classification function includes modifying the one or more weights.

3. The method of claim 1 , wherein the discrete variable optimization algorithm is a deterministic optimization algorithm.

4. The method of claim 3 , wherein the discrete variable optimization algorithm is a Gauss-Newton optimization algorithm.

5. The method of claim 3 , wherein the discrete variable optimization algorithm is a Levenberg-Marquardt optimization algorithm.

6. The method of claim 1 , wherein the discrete variable optimization algorithm is a stochastic algorithm.

7. The method of claim 6 , wherein the stochastic algorithm is stochastic gradient descent.

8. A system comprising:

a computer readable medium having instructions stored there on, which, when executed by a processor, cause the system to:

utilize a machine learning algorithm to generate a professionalism score for a digital image, wherein the professionalism score indicates a perceived professionalism of a human depicted in the digital image, the utilizing a machine learning algorithm comprising:

a training mode where a plurality of sample images with labeled professionalism scores are used to train a classification function in a model that produces as professionalism score as output;

an analysis mode where the model is used to generate a professionalism score for the digital image; and

using the professionalism score for the digital image as an input to a discrete variable optimization algorithm to determine an optimum cropped version of the digital image from a plurality of possible cropped versions of the digital image using the classification function.

9. The system of claim 8 , wherein the classification function contains one or more weights, and wherein the training of the classification function includes modifying the one or more weights.

10. The system of claim 8 , wherein the discrete variable optimization algorithm is a deterministic optimization algorithm.

11. The system of claim 10 , wherein the discrete variable optimization algorithm is a Gauss-Newton optimization algorithm.

12. The system of claim 10 , wherein the discrete variable optimization algorithm is a Levenberg-Marquardt optimization algorithm.

13. The system of claim 8 , wherein the discrete variable optimization algorithm is a stochastic algorithm.

14. The system of claim 13 , wherein the stochastic algorithm is stochastic gradient descent.

15. A non-transitory machine-readable storage medium comprising instructions, which when implemented by one or more machines, cause the one or more machines to perform operations for automatically performing optimal cropping on a digital image to improve perceived professionalism of a subject of the image, the operations comprising:

utilizing a machine learning algorithm to generate a professionalism score for the digital image, wherein the professionalism score indicates a perceived professionalism of a human depicted in the digital image, the utilizing a machine learning algorithm comprising:

a training mode where a plurality of sample images with labeled professionalism scores are used to train a classification function in a model that produces as professionalism score as output;

an analysis mode where the model is used to generate a professionalism score for the digital image; and

using the professionalism score for the digital image as an input to a discrete variable optimization algorithm to determine an optimum cropped version of the digital image from a plurality of possible cropped versions of the digital image using the classification function.

16. The non-transitory machine-readable storage medium of claim 15 , wherein the classification function contains one or more weights, and wherein the training of the classification function includes modifying the one or more weights.

17. The non-transitory machine-readable storage medium of claim 15 , wherein the discrete variable optimization algorithm is a deterministic optimization algorithm.

18. The non-transitory machine-readable storage medium of claim 17 , wherein the discrete variable optimization algorithm is a Gauss-Newton optimization algorithm.

19. The non-transitory machine-readable storage medium of claim 17 , wherein the discrete variable optimization algorithm is a Levenberg-Marquardt optimization algorithm.

20. The non-transitory machine-readable storage medium of claim 15 , wherein the discrete variable optimization algorithm is a stochastic algorithm.