Aesthetic Learning for Automated Image Capture Control
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Solution Overview
Problem
Existing automated image capture systems rely on pre-defined rules and 'one size fits all' approaches, which are restrictive and fail to adapt to different aesthetic styles, limiting their ability to produce high-quality images with specific aesthetic qualities.
Innovation Solution
The system employs pattern recognition techniques to learn visual features from a set of images representing a desired aesthetic style, allowing it to generate parameter suggestions for camera, lighting, and subject controls to achieve the desired aesthetic, using machine learning algorithms like neural networks to analyze and compare input images to exemplar and variation images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If pre-defined rules and algorithms are used for automated image capture control, then the system is simple to operate and provides consistent results, but the system lacks adaptability to different aesthetic styles and is overly restrictive
Solution Approach 1:
The system changes the fundamental parameter of aesthetic control from fixed pre-defined rules to dynamic machine-learned parameters. By training neural networks on datasets of images with desired aesthetic qualities, the system learns to adjust camera parameters (exposure, focus, composition) based on the specific aesthetic style being pursued, rather than applying universal rules to all situations.
Solution Approach 2:
The patent replaces the mechanical system of pre-programmed rule-based control with an intelligent system using machine learning and neural networks. The system substitutes explicit algorithmic rules with learned patterns from training data, allowing the camera to adaptively determine optimal parameters based on the input scene and desired aesthetic style.
2Manufacturing precision
If one size fits all rules are applied for auto-focus, auto-exposure, and white balance, then the system is easy to manufacture and operate, but the system produces generic results that do not achieve specific aesthetic qualities
Solution Approach 1:
The system performs preliminary action by pre-training the machine learning models on extensive datasets of images with desired aesthetic qualities before actual image capture. This training phase embeds aesthetic knowledge into the system, so that during operation, the camera can directly apply learned patterns without requiring users to manually configure complex aesthetic parameters.
Solution Approach 2:
The system enables self-service by allowing the camera to automatically determine optimal capture parameters based on the trained aesthetic model. The machine learning system independently analyzes the input scene and selects appropriate camera settings without requiring user intervention or expertise in photographic techniques.
3Difficulty of detecting and measuring
If conventional aesthetic analysis relies on human-rated databases and scoring algorithms, then the system is simple to implement, but the system only scores images rather than providing parameter suggestions for improvement
Solution Approach 1:
The system implements feedback by using the trained aesthetic model to analyze captured images and generate specific parameter suggestions for improvement. Rather than merely scoring images against predefined criteria, the system provides actionable feedback on how to adjust camera parameters to better achieve the desired aesthetic style, creating a closed-loop system for continuous improvement.
Data Source
AI summary
Methods and systems for generating image capture device parameter suggestions that would produce an image in, or closer to, a desired aesthetic style. In particular, the systems and methods described herein include pattern recognition techniques which are utilized to extract visual features from a set of images, those features defining an aesthetic style. The set of images comprise exemplars of said aesthetic style as well as images representing a plurality of variations in image capture parameters. An algorithm is trained to discriminate between exemplar and variation images based on their extracted visual features. When the system is presented with an input image the same visual features are extracted from it and are compared to the characteristic visual features of the exemplar and variation images with the trained discriminator algorithm. The similarity of the input image to exemplar and variation images are used to generate image capture device parameter suggestions.