Optimal image transformation based on professionalism score of subject
First Claim
1. A computerized method for automatically performing an image transformation on 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 continuous variable optimization algorithm to determine an optimum version of the digital image from a plurality of possible versions of the digital image on which one or more image transformations have been performed, using the classification function, wherein the continuation variable optimization algorithm uses a deep convolutional neural network (DCNN) by passing the digital image to a convolutional layer, generating output, passing the output from the convolutional layer to a nonlinearity layer, generating output, passing the output from the nonlinearity layer to a pooling layer, generating output, and passing output from the nonlinearity layer to a classification layer.
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Abstract
In an example embodiment, an image transformation is automatically performed on a digital image to improve perceived professionalism of a subject of the image. A machine learning algorithm is utilized to generate a professionalism score for 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. Then the professionalism score is used as an input to a continuous variable optimization algorithm to determine an optimum version of the digital image from a plurality of possible versions of the digital image on which one or more image transformations have been performed, using the classification function.
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Citations
20 Claims
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1. A computerized method for automatically performing an image transformation on a digital image to improve perceived professionalism of a subject of the image, the method comprising:
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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 continuous variable optimization algorithm to determine an optimum version of the digital image from a plurality of possible versions of the digital image on which one or more image transformations have been performed, using the classification function, wherein the continuation variable optimization algorithm uses a deep convolutional neural network (DCNN) by passing the digital image to a convolutional layer, generating output, passing the output from the convolutional layer to a nonlinearity layer, generating output, passing the output from the nonlinearity layer to a pooling layer, generating output, and passing output from the nonlinearity layer to a classification layer. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10)
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11. 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 an image transformation on a digital image to improve perceived professionalism of a subject of the image, the operations comprising:
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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 continuous variable optimization algorithm to determine an optimum version of the digital image from a plurality of possible versions of the digital image on which one or more image transformations have been performed, using the classification function, wherein the continuation variable optimization algorithm uses a deep convolutional neural network (DCNN) by passing the digital image to a convolutional layer, generating output, passing the output from the convolutional layer to a nonlinearity layer, generating output, passing the output from the nonlinearity layer to a pooling layer, generating output, and passing output from the nonlinearity layer to a classification layer. - View Dependent Claims (12, 13, 14, 15, 16, 17, 18, 19, 20)
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Specification