Image Analysis System Using Iterative Weight Assignment
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Solution Overview
Problem
The selection of images for visual communication in marketing and advertising is often subjective and lacks critical information, leading to ineffective communication and substantial financial losses due to poor audience impact and reduced consumer trust.
Innovation Solution
A method and system that analyze images by collecting reference images, extracting features, assigning weights, determining image scores, and providing improvement proposals for test images based on iterative comparisons with ranking data, enabling robust and efficient image analysis for effective visual communication.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If image selection is made by marketing professionals based on subjective feelings, then the selection process is simple and quick, but the effectiveness of selected images is uncertain and may lead to financial losses
Solution Approach 1:
The system implements feedback by collecting actual audience response data (click-through rates, engagement metrics, survey results) and using this feedback to iteratively improve image selection. The feedback loop allows the system to learn from past performance and continuously optimize future image choices, transforming subjective selection into data-driven decision making.
Solution Approach 2:
The patent replaces the mechanical system of human subjective judgment with an automated computational system that uses machine learning algorithms, image analysis, and data processing to objectively evaluate and select images based on measured audience response, eliminating reliance on professional intuition.
2Productivity
If no systematic analysis is used for image selection, then the process is fast and inexpensive, but important information is missing and consumer trust may be reduced
Solution Approach 1:
The system performs preliminary action by pre-collecting and analyzing audience response data before final image selection. It proactively gathers metrics from multiple sources, pre-processes the data, and prepares evaluation models in advance, so that when images need to be selected, the analytical framework is already ready and populated with relevant information.
Solution Approach 2:
The system achieves universality by integrating multiple data collection methods (web analytics, social media metrics, survey tools), various image analysis techniques, and diverse evaluation criteria into a single multi-functional platform that can handle different types of images and audience responses through a unified analytical framework.
3Measurement precision
If iterative weight assignment and scoring is performed for image analysis, then image selection accuracy is improved, but the complexity of the system increases
Solution Approach 1:
The system applies segmentation by breaking down the complex image evaluation process into distinct modular components: feature extraction modules, weight assignment modules, scoring modules, and ranking modules. Each component handles a specific aspect of the analysis independently, making the overall complex system manageable through functional decomposition.
Solution Approach 2:
The system implements dynamics by making the weight assignments adaptive rather than static. Weights are dynamically adjusted based on the specific image being evaluated, the context, and learned from historical performance data. This dynamic approach allows the system to handle diverse images with varying characteristics while maintaining accuracy.
Data Source
AI summary
A method executed by one or more processors for analyzing at least one test image. The method includes collecting a plurality of reference images from at least one image source, extracting image features from the plurality of reference images, assigning weights to the image features extracted, determining image scores for the plurality of reference images, iteratively performing the assigning of the weights and the determining of the image scores, extracting image features from the at least one test image, assigning weights to the image features of the at least one test image, determining an image score for the at least one test image, based upon the weights assigned to the image features of the at least one test image, and providing an improvement proposal for the at least one test image, based upon the image score determined for the at least one test image.