DCNN Image Professionalism Scoring and Optimization
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Solution Overview
Problem
The challenge lies in enhancing the quality of digital images used in professional contexts, as existing technologies struggle to quantify and improve the 'professionalism' of images, which is influenced by various subtle factors like clothing, angle, and background, and these factors can vary by industry and location.
Innovation Solution
A Deep Convolutional Neural Network (DCNN) is employed to generate professionalism scores for digital images by identifying relevant features, allowing for automatic image transformations such as cropping and rotation to enhance perceived professionalism, without human intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If automated image transformation is applied to improve professionalism score, then image quality is improved, but the complexity of the system increases due to DCNN and optimization algorithms
Solution Approach 1:
The system performs self-service by automatically evaluating its own output and iteratively optimizing transformations without human intervention. The DCNN evaluates professionalism scores and the optimization algorithm autonomously adjusts transformation parameters to maximize the score, creating a self-improving system that resolves the complexity issue through automation.
Solution Approach 2:
The patent replaces manual mechanical image editing with an automated computational system. Instead of human operators physically adjusting image parameters, a DCNN-based computational system with optimization algorithms automatically performs transformations, substituting mechanical human operation with intelligent automated processing.
2Manufacturing precision
If multiple transformation parameters are optimized to maximize professionalism score, then image professionalism is improved, but the computational time and resources increase
Solution Approach 1:
The system performs preliminary action by pre-defining a finite set of discrete transformation parameters and their possible values before optimization begins. This pre structuring of the search space allows the optimization algorithm to efficiently evaluate combinations without exhaustive search, reducing computational time while still achieving high professionalism scores.
Solution Approach 2:
The patent applies parameter changes by systematically varying transformation parameters (crop coordinates, rotation angles, scaling factors) to find optimal values. The DCNN evaluates different parameter combinations and the optimization algorithm adjusts parameters iteratively, changing physical image parameters to maximize professionalism while managing computational resources.
Data Source
AI summary
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.