Image-Based Ad Targeting in Video Content
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Solution Overview
Problem
Current advertising technologies fail to effectively target advertisements based on visual content, such as images and videos, leading to inefficiencies in reaching receptive audiences.
Innovation Solution
An image-based ad targeting system that identifies regions-of-interest within images or video frames and matches them with stored associations to select and display relevant advertisements, using object recognition techniques and bid-based selection methods.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional advertising methods are used, then ads can be displayed to users, but the targeting precision and relevance to user interests are insufficient
Solution Approach 1:
The system segments video content into individual frames and identifies specific regions-of-interest within each frame. This segmentation allows the system to focus on particular visual elements (products, objects, scenes) rather than treating the entire video as a single unit, thereby improving the precision of ad targeting based on specific visual content.
Solution Approach 2:
The system introduces an intermediary image-matching mechanism that bridges the gap between visual content and advertisement selection. By comparing frames from the video against a database of stored images, the system identifies matching content and uses this information to select relevant advertisements, thus recovering and utilizing user interest information that would otherwise be lost.
2Adaptability or versatility
If image-based ad targeting is implemented, then ad relevance to visual content improves, but system complexity increases due to image processing requirements
Solution Approach 1:
The system performs preliminary actions by pre-processing video content into extractable frames and pre-building a database of stored images for comparison. This preparation work is done in advance, allowing the actual ad selection process to rely on efficient image matching rather than complex real-time analysis, thereby reducing the computational complexity during advertisement delivery.
Solution Approach 2:
The system uses image copying and matching techniques where frames from the video are compared against copies of stored images in a database. By utilizing image matching rather than complex semantic analysis, the system achieves adaptable targeting while maintaining relatively simple processing requirements.
3Measurement precision
If real-time image matching is performed during video playback, then ad selection accuracy improves, but processing time and computational resources increase
Solution Approach 1:
The system applies partial action by selecting and processing only certain frames from the video rather than analyzing every single frame. By identifying key frames that contain regions-of-interest and matching only these selected frames against the stored image database, the system maintains matching accuracy while significantly reducing the overall processing time and computational resource requirements.
Data Source
AI summary
Sponsored-content may be placed based on images in video content. A first image in a frame of a video content item is identified. The first image is matched with a second stored image. A sponsored-content item to be presented is selected based on an association between the second stored image and the sponsored-content item.