Multimedia Feature Clustering for Accurate Resource Classification
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Solution Overview
Problem
Existing multimedia resource classification methods suffer from reduced accuracy due to different features of the same resource being classified into different types, leading to inefficiencies in personalized recommendations and resource screening.
Innovation Solution
A method involving feature extraction, clustering, and non-local feature description using NetVLAD and association information to generate target feature description, which includes cluster description and association information, enhancing the representation and accuracy of multimedia resource classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If different features of the same multimedia resource are classified separately, then feature diversity is captured, but classification accuracy deteriorates
Solution Approach 1:
The patent merges multiple features of the same multimedia resource into a unified classification result. The classification server integrates results from different feature types (audio, video, text) to produce a single consistent classification, resolving the contradiction by combining separate feature analyses rather than treating them independently.
Solution Approach 2:
The patent introduces a classification server as an intermediary that coordinates between different feature extraction processes and the final classification result. This mediator integrates the classification results from multiple feature types, ensuring that diverse features contribute to a unified accurate classification rather than conflicting separate classifications.
2Ease of operation
If traditional classification methods are used, then processing simplicity is maintained, but classification accuracy deteriorates
Solution Approach 1:
The patent segments the classification process into distinct modular components: feature extraction modules for different media types (audio, video, text), a classification server for integrating results, and a result processing module. This segmentation maintains operational simplicity through clear module boundaries while improving accuracy through specialized processing in each segment.
Solution Approach 2:
The patent changes the parameters of the classification system by introducing multiple feature types and their corresponding extraction parameters, rather than using a single traditional classification parameter. This allows the system to maintain simplicity in the overall process flow while achieving higher accuracy through multi-parameter feature analysis.
3Quantity of substance
If multiple features are extracted and processed separately, then feature completeness is improved, but information redundancy increases
Solution Approach 1:
The patent extracts only the essential and relevant features from multimedia resources through specialized extraction modules for audio, video, and text. By selectively extracting meaningful features rather than processing all possible data, the system achieves feature completeness while avoiding redundancy of irrelevant information.
Solution Approach 2:
The patent performs preliminary feature extraction and filtering before the main classification process. The feature extraction modules pre-process the multimedia data to identify and extract only the most relevant features, reducing redundancy before classification occurs and improving both completeness and efficiency.
Data Source
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AI summary
This application discloses a multimedia resource classification method and apparatus, a computer device, and a storage medium. The method includes: obtaining a multimedia resource, and extracting a plurality of pieces of feature information of the multimedia resource; clustering the plurality of pieces of feature information to obtain at least one cluster set, and determining cluster description information of each cluster set, the each cluster set including at least one piece of feature information, and each piece of cluster description information being used for indicating a feature of one cluster set; determining at least one piece of target feature description information of the multimedia resource based on the cluster description information of each cluster set, each piece of target feature description information being used for representing an association between one piece of cluster description information and the remaining cluster description information; and classifying the multimedia resource based on the at least one piece of target feature description information of the multimedia resource, to obtain a classification result of the multimedia resource.