Media Snapshot Search Using Context-Based Data Subsetting
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Solution Overview
Problem
Image-based search systems face inefficiencies and inaccuracies due to the large number of images that need to be compared, leading to slow performance and potential false matches when searching for features in media content.
Innovation Solution
The system utilizes context information associated with media content to narrow the search by identifying a subset of searchable data, supplemented with user input and supplemental data to focus the search, allowing for faster and more accurate identification of features in captured snapshot images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the search system compares the captured image to a large collection of searchable data, then the search can be more comprehensive, but the search speed decreases and the system requires more memory and time
Solution Approach 1:
The patent divides the large collection of searchable data into multiple subsets based on context information (e.g., media content type, genre, metadata). The search system then compares the captured image only to relevant subsets rather than the entire collection, reducing the number of comparisons needed while maintaining search comprehensiveness within the appropriate context.
Solution Approach 2:
The patent introduces context information as an intermediary element that mediates between the captured image and the searchable data collection. This context information (such as media content metadata, genre, or associated data) is used to filter and select relevant data subsets, enabling the system to efficiently narrow down the search space without losing accuracy.
2Quantity of substance
If the search system uses a large collection of images for matching, then more potential matches are available, but the chance of returning false matches increases
Solution Approach 1:
The patent extracts and removes irrelevant data from the searchable collection by using context information to filter out images that do not belong to the appropriate category or context. This extraction of irrelevant data reduces the number of false matches while maintaining the quantity of relevant potential matches.
Solution Approach 2:
The patent applies different search strategies to different local regions or subsets of the data collection based on their relevance to the captured image. By identifying which subsets of data are most relevant to the current search context, the system can focus computational resources on the most promising matches, improving accuracy while still considering enough data to avoid false negatives.
3Measurement precision
If the search system analyzes all available features in the captured image, then the search can be more thorough, but the processing time and computational resources increase
Solution Approach 1:
The patent applies partial action by selecting and analyzing only the most relevant features from the captured image based on context information. Instead of analyzing all possible features, the system identifies and processes a subset of features that are most likely to yield accurate results given the context, reducing processing time while maintaining sufficient measurement precision.
Data Source
AI summary
Systems and methods are provided for performing a search based on a snapshot image captured from media content presented to a user. The snapshot image contains features of the media content that the user wishes to target for the search. A search system recognizes features of the snapshot image and creates a search query based on the snapshot image. The search query is used to identify features of the snapshot image, and search results related to the identified features are presented to the user. Supplemental data or user input received with the snapshot image may be used in analyzing and identifying features of the snapshot image.