Visual Feature Matching for Image-Video Association
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Solution Overview
Problem
Internet search engines face difficulties in identifying and scoring relevant still images and videos due to limited associated text, making it hard to determine accurate relationships between them.
Innovation Solution
A method that involves extracting features from still images and video frames, comparing these features to determine visual similarity, and maintaining a data store to associate related images and videos, which also augments metadata and presents search results with visual relationship indicators.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If text-based identification methods are used for images and videos, then search results can be generated, but accurate identification and scoring become difficult when little text is associated with the media
Solution Approach 1:
The patent introduces an intermediary system that uses visual feature extraction and comparison as a mediator between the image/video content and the search query. Instead of relying directly on text metadata, the system extracts visual features from images and videos, compares them with query features, and uses this visual similarity as an intermediary mechanism to identify relevant media even when text information is minimal or absent.
2Reliability
If visual feature extraction and comparison is implemented, then accurate association between images and videos can be achieved, but system complexity increases
Solution Approach 1:
The patent segments the complex task of image-video association into distinct modular components: feature extraction module, feature comparison module, and association determination module. Each component handles a specific aspect of the process, making the overall system more manageable and maintainable despite the increased complexity required for accurate visual matching.
Solution Approach 2:
The patent creates a universal feature extraction and comparison framework that can handle both images and videos through a common interface. The system extracts visual features from both media types using similar methodologies and compares them using a unified similarity metric, allowing the same core mechanism to serve multiple functions across different media types.
3Measurement precision
If all frames of a video are analyzed, then complete visual comparison is achieved, but processing time and computational resources increase significantly
Solution Approach 1:
The patent extracts and analyzes only the essential visual features from video frames rather than processing all frame data. By identifying and extracting key visual characteristics (such as dominant colors, shapes, textures, and object features) from representative frames, the system achieves accurate visual similarity comparison while significantly reducing the computational burden of analyzing every single frame in a video sequence.
Solution Approach 2:
The patent applies partial action by analyzing a subset of video frames rather than all frames. The system selects key frames or representative moments from videos and performs feature extraction only on these partial samples, which is sufficient to capture the essential visual content for comparison purposes without the excessive computational cost of processing the entire video sequence.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for associating still images and videos. One method includes receiving a plurality of images and a plurality of videos and determining whether the images are related to the videos. The determining includes, for an image and a video, extracting features from the image and extracting features frames of the video, and comparing the features to determine whether the image is related to the video. The method further includes maintaining a data store storing data associating each image with each video determined to be related to the image.