Multimedia Labeling Hypernym Filtering
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Solution Overview
Problem
Current multimedia labeling methods, including manual, automatic, and semi-automatic approaches, are inefficient and produce noisy or irrelevant results, making it difficult for users to effectively label and search multimedia objects.
Innovation Solution
A semi-automatic labeling method that filters suggested labels to provide a second set of hypernyms, allowing users to iteratively refine their annotations, with the option to update labels based on user selection, reducing noise and improving relevance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If semi-automatic labeling methods are used to suggest labels based on past user labels, then the number of suggested labels increases, but the relevance and quality of labels deteriorates due to noise
Solution Approach 1:
The patent extracts only the hypernym labels from the first set of suggested labels to form a second set. This filtering process removes specific, potentially noisy labels while retaining general, reliable labels that are more likely to be relevant to the user's needs.
Solution Approach 2:
The patent changes the semantic parameter of the labels by transforming specific labels into their hypernym forms. This parameter transformation increases the generality and reliability of the suggested labels while maintaining their usefulness for categorization.
2Measurement precision
If manual labeling is used to ensure accurate annotations, then labeling precision improves, but time consumption and labor increase significantly
Solution Approach 1:
The system performs preliminary automated labeling by generating a first set of labels based on analysis of the multimedia object and past user behavior. This preliminary action reduces the workload for manual labeling while maintaining accuracy through subsequent user selection from the pre-filtered second set of hypernym labels.
Solution Approach 2:
The patent introduces an intermediary filtering process that generates hypernym labels as a middle layer between automated label generation and user selection. This intermediary step bridges the gap between automated efficiency and manual precision by providing users with a curated, simplified set of relevant labels.
3Productivity
If automatic labeling methods are used to analyze multimedia content, then labeling speed increases, but the robustness and user satisfaction decrease due to limited label categories
Solution Approach 1:
The patent makes the labeling system dynamic by iteratively updating the second set of hypernym labels based on user selections. The system adapts to user preferences and behavior patterns over time, improving reliability and user satisfaction while maintaining high labeling speed through automated processes.
Solution Approach 2:
The system incorporates feedback loops where user selections from the second set of labels are used to update and refine future label suggestions. This feedback mechanism improves the system's understanding of user needs, enhancing both the speed and reliability of labeling over time.
Data Source
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AI summary
The method for labeling at least one multimedia object belonging to a user includes: - a step (E40) of determining a first set of labels for that multimedia object by applying (E30) at least one predetermined selection rule; - a step (E60) of obtaining a second set of labels for that multimedia object from the first set of labels in which at least one label is a hypernym of a label from the first set; - a step (E70) of providing the second set of labels to the user; - if a label (E80) is selected by the user: o a step (E100) of labeling the multimedia object with the selected label; and o a step (E110) of updating the second set of labels according to the label selected by the user, the steps of providing, and where appropriate of labeling and updating being repeated with the updated second set of labels.