Metadata Augmentation for Video Search Accuracy
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Solution Overview
Problem
User-supplied label metadata in video hosting services is often incomplete or inaccurate, leading to reduced user experience due to missed relevant videos or false findings during searches.
Innovation Solution
A metadata augmentation system that determines similarities between digital objects to propagate metadata from objects with sufficient metadata to those lacking it, using intrinsic and extrinsic factors, and trains classifiers to enhance metadata accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If user-supplied label metadata is used for video search and classification, then the system can provide basic search functionality, but the metadata quality deteriorates due to incompleteness, inaccuracies, and intentional false labeling
Solution Approach 1:
The patent introduces an intermediary metadata augmentation system that mediates between user-supplied metadata and the actual video content. This system uses classifiers trained on video features (audio, visual, textual) to generate augmented metadata that complements or corrects user-supplied labels, thereby improving overall metadata quality without relying solely on user input
Solution Approach 2:
The system enables videos to self-describe their content through automatically generated metadata. By extracting features directly from the video content itself (audio transcripts, visual analysis, text overlays), the video effectively annotates itself, reducing dependence on potentially unreliable user-supplied metadata
2Productivity
If user-supplied metadata is accepted as-is, then the system maintains simplicity and low processing overhead, but search accuracy and user experience deteriorate due to missed relevant videos and false findings
Solution Approach 1:
The system performs preliminary metadata augmentation during video processing or indexing, before search operations occur. Classifiers are pre-trained on video features, and metadata is augmented in advance, so that when search queries are executed, high-quality metadata is already available, maintaining fast search performance while improving accuracy
Data Source
AI summary
A metadata augmentation system determines similarities between digital objects, such as digital videos, that may or may not have metadata associated with them. Based on the determined similarities, the metadata augmentation system augments metadata of objects, such as augmenting metadata of objects lacking a sufficient amount of metadata with metadata from other objects having a sufficient amount of metadata.In one embodiment, the similarities are used to determine training sets for training of classifiers that output degrees of more specific similarities between the corresponding video and an arbitrary second video. These classifiers are then applied to add metadata from one video to another based on a degree of similarity between the videos, regardless of their respective locations within the object similarity graph.