Media Identification via Multi-Bucket Hash Lookup
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Solution Overview
Problem
Existing media identification systems face inefficiencies in identifying media due to limitations in bucket size during hash table lookups, leading to exclusion of data and reduced accuracy in matching media fingerprints.
Innovation Solution
Implementing a multi-round limited bucket size lookup method that combines initial and secondary searches using different bucket sizes, allowing for increased search depth and accuracy by boosting hash hits for candidate matches that initially do not meet the threshold.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a single bucket size is used in hash table lookup, then the search process is simple and fast, but data is excluded and matching accuracy is reduced
Solution Approach 1:
The patent segments the hash table into multiple buckets with different size parameters. Instead of using a single uniform bucket size, the system creates multiple buckets with varying capacities, allowing different portions of the data to be stored and retrieved with optimal precision for their respective characteristics. This segmentation enables the system to capture more reference matches without overwhelming complexity in any single bucket.
Solution Approach 2:
The patent changes the parameter of bucket size from a fixed single value to multiple variable values. By implementing multiple buckets with different size parameters, the system can adjust the storage capacity and retrieval characteristics of each bucket to optimize matching accuracy for different types of media data, thereby improving overall identification precision without requiring a complete redesign of the hash table structure.
2Measurement precision
If multiple buckets of different sizes are used, then matching accuracy is improved, but the search process becomes more complex
Solution Approach 1:
The patent applies preliminary action by pre-organizing media reference data into multiple buckets with different size parameters before the actual search process. This pre-segmentation and pre-categorization allows the search algorithm to quickly determine which buckets to query based on the characteristics of the query data, avoiding the need to search through all buckets uniformly. This preliminary organization maintains search efficiency while enabling the benefits of multiple bucket sizes.
Data Source
AI summary
Methods, apparatus, systems, and articles of manufacture are disclosed to improve media identification. An example apparatus includes a hash handler to generate a first set of reference matches by performing hash functions on a subset of media data associated with media to generate hashed media data based on a first bucket size, a candidate determiner to identify a second set of reference matches that include ones of the first set, the second set including ones having first quantities of hits that did not satisfy a threshold, determine second quantities of hits for ones of the second set by matching ones to the hash tables based on a second bucket size, and identify one or more candidate matches based on at least one of (1) ones of the first set or (2) ones of the second set, and a report generator to generate a report including a media identification.