System And Method For Extracting Object Information From Digital Images To Evaluate For Realism

Smart overview of the Invention

The system and method described focus on evaluating the realism of digital images generated by AI models. The process begins by receiving prompts, which are then sent to both a search engine and a generative AI model. The search engine provides a reference image that serves as the ground truth, while the AI model produces synthesized images. These images are compared to determine their realism score, which reflects how accurately they represent real-world content based on the reference image.

Technical Approach

The system employs deep learning techniques to assess image realism. It classifies pixels in both reference and synthesized images to identify corresponding objects. By extracting feature vector representations, such as Hu moments, it compares these objects even if they appear different due to perspective or scale changes. This comparison generates a realism score for each image, aiding users in selecting the most accurate representation of their desired scene.

Applications and Benefits

This approach offers significant advantages in improving generative AI model performance. By using realism scores, users can efficiently pre-select images that align closely with their intended goals. The system also assists in fine-tuning AI models by filtering out less accurate images, ensuring that only high-quality content is presented or used for further model training.

Implementation Details

The method involves submitting prompts to both a search engine and an AI model to receive reference and synthesized images. Using a deep learning-based instance segmentation model, it classifies objects within these images and extracts Hu Moments for comparison. Realism scores are computed based on these comparisons, allowing for ranking and selection of the most realistic images.

Conclusion

The disclosed system provides a robust framework for evaluating the realism of AI-generated images. By offering an automated approach to measure image accuracy against real-world content, it enhances user confidence in utilizing AI-generated visuals while streamlining the image selection process. This innovation not only improves current generative models but also paves the way for more reliable AI applications in various fields.