Multimedia Ontology Selection via Recursive Routing
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Solution Overview
Problem
Current automatic content tagging systems face inefficiencies in large-scale multimedia classification due to computational bottlenecks and the need for human intervention, as they struggle to select the appropriate set of classifiers based on context and ontologies, leading to compromised metadata quality and increased processing complexity.
Innovation Solution
A method using recursive routing selection to score and select appropriate ontologies and classifiers based on the context of multimedia artifacts, optimizing the evaluation of semantic elements and reducing the number of concepts evaluated, thereby enhancing semantic tagging without increasing processing complexity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If all available ontologies and classifiers are evaluated against multimedia artifacts, then comprehensive semantic tagging is achieved, but computational cost and processing time increase exponentially
Solution Approach 1:
The patent segments the large set of all available ontologies and classifiers into multiple smaller groups or subsets. Instead of evaluating all classifiers against all multimedia artifacts, the system divides the classification task into manageable segments, evaluating only relevant subsets of classifiers for each artifact based on preliminary analysis or contextual cues.
Solution Approach 2:
The patent applies partial action by evaluating only a selected portion of available classifiers rather than all classifiers. The system determines that evaluating a carefully selected subset of classifiers is sufficient to achieve the required tagging accuracy, avoiding the excessive computational cost of evaluating every possible classifier.
2Adaptability or versatility
If the number of semantic classifiers is increased to detect more concepts, then classification coverage improves, but system complexity and entropy increase
Solution Approach 1:
The patent creates a universal framework that can handle multiple domains and types of multimedia artifacts using a single integrated system. The ontology selection mechanism and classifier evaluation approach are designed to be domain-agnostic, allowing the same system structure to serve diverse classification needs without increasing complexity proportionally to the number of domains.
Solution Approach 2:
The patent introduces dynamic adaptation where the system adjusts the set of active classifiers based on the specific multimedia artifact being processed. Rather than maintaining a static, comprehensive set of all possible classifiers, the system dynamically selects and activates only those classifiers relevant to the current artifact's domain, type, and characteristics.
3Measurement precision
If manual selection of classifiers by human experts is used, then classification quality is maintained, but processing time and human resource requirements increase
Solution Approach 1:
The patent implements self-service by enabling the system to automatically select appropriate ontologies and classifiers without human intervention. The automated ontology selection mechanism and classifier evaluation process allow the system to make intelligent decisions about which classifiers to apply, replacing manual expert selection with autonomous automated decision-making.
Solution Approach 2:
The patent incorporates feedback mechanisms where the system learns from previous classification results and performance metrics. The ontology selection and classifier evaluation processes use feedback from artifact characteristics, preliminary analysis results, and historical performance data to automatically determine the most appropriate classifiers, improving quality over time without additional human input.
Data Source
AI summary
A method and apparatus is provided for automatically classifying a multimedia artifact based on scoring, and selecting the appropriate set of ontologies from among all possible sets of ontologies, preferably using a recursive routing selection technique. The semantic tagging of the multimedia artifact is enhanced by applying only classifiers from the selected ontology, for use in classifying the multimedia artifact, wherein the classifiers are selected based on the context of the multimedia artifact. One embodiment of the invention, directed to a method for classifying a multimedia artifact, uses a specified criteria to select one or more ontologies, wherein the specified criteria indicates the comparative similarity between specified characteristics of the multimedia artifact and each ontology. The method further comprises scoring and selecting one or more classifiers from a plurality of classifiers that respectively correspond to semantic element of the selected ontologies, and evaluating the multimedia artifact using the selected classifiers to determine a classification for the multimedia artifact.