Optimal cropping of digital image based on professionalism score of subject
First Claim
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.
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Abstract
In an example embodiment, an optimal cropping of a digital image is determined. A machine learning algorithm is used 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; and 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 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.
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Citations
20 Claims
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1. A computerized method for automatically cropping 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 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. - View Dependent Claims (2, 3, 4, 5, 6, 7)
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8. A system comprising:
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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. - View Dependent Claims (9, 10, 11, 12, 13, 14)
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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:
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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 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. - View Dependent Claims (16, 17, 18, 19, 20)
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Specification