Neural Network Image Curation for Aesthetic Quality
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Solution Overview
Problem
Conventional image curation techniques often result in the overrepresentation of redundant images and poor quality images due to reliance on cues like time, location, and visual similarity, failing to effectively capture the aesthetics and quality of images in a repository.
Innovation Solution
A neural network-based image curation system that calculates scores for image and face aesthetics, ranks images based on these scores, and selects representative images while avoiding visual similarity to existing selections, ensuring a diverse and high-quality representation of the repository.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional techniques use cues like time, location, and visual similarity for image curation, then the curation process is simple and fast, but redundant images are overrepresented and quality is poor
Solution Approach 1:
The patent transforms the image curation problem from using simple cues (time, location) to using learned aesthetic parameters through neural networks. The system changes the evaluation parameters from metadata-based to appearance-based, analyzing visual characteristics like composition, lighting, and subject matter to determine image quality and representativeness.
Solution Approach 2:
The patent replaces conventional mechanical/heuristic methods (rule-based filtering using time and location cues) with a neural network-based aesthetic evaluation system. This substitution enables the system to automatically assess image quality and diversity without manual intervention or simple metadata rules.
2Manufacturing precision
If manual image curation is performed, then image selection quality can be high, but the process becomes extremely time consuming
Solution Approach 1:
The patent implements self-service by enabling the system to automatically evaluate and select representative images using neural network-based aesthetic assessment. The system serves itself by autonomously analyzing image characteristics, ranking candidates, and making selection decisions without human intervention, thereby achieving both high quality and efficiency.
Solution Approach 2:
The patent replaces manual human curation with an automated neural network system that evaluates aesthetic quality. This substitution maintains the quality assessment capability of manual review while eliminating the time cost, as the neural network can process numerous images rapidly without fatigue or inconsistency.
3Productivity
If conventional automatic techniques give greater weight to recent images, then the curation process is efficient, but images from earlier time periods are not captured
Solution Approach 1:
The patent changes the weighting parameters from time-based (recent images favored) to aesthetic-quality-based (most representative images favored). The neural network evaluates each image's aesthetic merit independently of its timestamp, allowing images from any time period to be selected based on their visual quality and representativeness rather than their recency.
Solution Approach 2:
The patent inverts the conventional approach by not prioritizing recent images based on time, but rather prioritizing images based on their aesthetic quality and representativeness regardless of when they were taken. This inversion allows the system to capture diverse temporal representation by selecting the best images from all time periods rather than favoring the most recent.
Data Source
AI summary
Neural network image curation techniques are described. In one or more implementations, curation is controlled of images that represent a repository of images. A plurality of images of the repository are curated by one or more computing devices to select representative images of the repository. The curation includes calculating a score based on image and face aesthetics, jointly, for each of the plurality of images through processing by a neural network, ranking the plurality of images based on respective said scores, and selecting one or more of the plurality of images as one of the representative images of the repository based on the ranking and a determination that the one or more said images are not visually similar to images that have already been selected as one of the representative images of the repository.