Heuristics-Based Copy Space Detection in Images
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Solution Overview
Problem
Current methods for identifying suitable spaces in images or video frames for overlaying media content are manual, time-consuming, and inefficient, especially when dealing with large volumes of images or video frames, as they do not effectively automate the process of distinguishing between visually essential and non-essential areas.
Innovation Solution
A system that uses image processing techniques such as edge detection, saliency filtering, and machine learning for identifying unoccupied spaces, followed by heuristic rules to select optimal bounding boxes for media content insertion, ensuring that the overlay does not disrupt essential visual elements, and automatically proposes and selects regions for media content placement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual methods are used to identify copy space, then accuracy in distinguishing visually essential and non-essential areas is maintained, but time consumption and inefficiency increase significantly when dealing with large volumes of images
Solution Approach 1:
The patent replaces manual mechanical marking of copy space with an automated computer-based system that uses image processing techniques including edge detection, saliency filtering, and machine learning algorithms to automatically identify and mark copy space regions in images and video frames
Solution Approach 2:
The system enables automatic self-service by processing images through multiple automated stages: edge detection to identify boundaries, saliency filtering to detect important regions, machine learning classification to distinguish essential from non-essential areas, and automatic bounding box generation to define copy space regions without human intervention
2Productivity
If automated image processing techniques are used to identify copy space, then productivity and efficiency are improved, but the complexity of the system increases
Solution Approach 1:
The patent divides the complex task of copy space identification into multiple sequential segments: edge detection stage, saliency filtering stage, machine learning classification stage, and bounding box generation stage. Each segment handles a specific aspect of the problem, making the overall complex system manageable through modular processing steps
Solution Approach 2:
The patent introduces intermediate processing results between different stages: edge maps from edge detection serve as input to saliency filtering, saliency maps serve as input to machine learning classifiers, and classified regions serve as input to bounding box generation. These intermediaries facilitate the complex processing chain while maintaining clear data flow and enabling independent optimization of each stage
3Productivity
If automated detection is implemented, then time efficiency is improved, but the ability to accurately distinguish visually essential elements may be compromised
Solution Approach 1:
The patent merges multiple complementary detection techniques: edge detection algorithms identify structural boundaries, saliency filtering highlights visually prominent regions, and machine learning classifiers integrate these signals to distinguish essential from non-essential areas. The combination of these methods compensates for individual weaknesses and achieves both speed and accuracy
Solution Approach 2:
The system employs feedback mechanisms where machine learning models are trained on labeled data to learn accurate distinctions between essential and non-essential regions, and where the classification results can be refined through iterative processing. The feedback from trained models improves the accuracy of automated detection while maintaining high processing speeds
Data Source
AI summary
Disclosed herein are system, method and computer readable storage medium for detecting space suitable for overlaying media content onto an image. The system receives an image which may be an image or a video frame. The image is processed using a number of image processing techniques in order to automatically propose spaces for inserting media content onto the image. The proposed spaces may then be further analyzed using a heuristics-based approach to select bounding boxes for inserting media content. Subsequently, one or more media content items may be selected for insertion onto the bounding boxes on the image. The system may then cause a display of the image with the selected media content item overlaid onto the image within the selected bounding boxes.