Video Feature Extraction and Metadata Matching for Cloud Content Organization
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Solution Overview
Problem
The rapid growth of internet video consumption poses challenges in efficiently organizing and managing multimedia content, particularly in cloud computing environments, where existing systems struggle to effectively categorize and retrieve videos based on features and metadata.
Innovation Solution
A method and system that involves extracting features from input videos and comparing them with metadata in a database to match and incorporate them into a tree indexing structure, allowing for efficient organization, search, and retrieval of multimedia content, using processors and metadata database storage to facilitate real-time indexing and adaptive streaming.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If video content is continuously streamed in real time to meet growing internet video consumption, then user entertainment and service quality are improved, but system complexity and management difficulty increase
Solution Approach 1:
The patent segments video content into distinct features (visual, audio, textual) and organizes them separately in a database. Each video is broken down into identifiable components that can be independently indexed and retrieved, transforming the unmanageable stream of video data into structured, searchable segments.
Solution Approach 2:
The patent introduces an intermediary feature extraction and matching system between the video streaming infrastructure and the content management database. This intermediary layer automatically extracts features from streaming videos, compares them against stored metadata, and performs matching without requiring manual intervention, thus managing complexity while maintaining high streaming capacity.
2Measurement precision
If feature extraction and metadata comparison is performed for every input video, then video organization accuracy is improved, but processing time increases
Solution Approach 1:
The patent performs preliminary action by pre-extracting and storing metadata features from videos before they need to be matched. The system maintains a database of pre-processed video features that can be quickly compared against new incoming videos, eliminating the need for complete re-extraction and reducing processing time while maintaining matching accuracy.
Solution Approach 2:
The patent applies partial action by extracting only the most discriminative and relevant features from videos rather than analyzing every aspect. This selective feature extraction achieves sufficient matching accuracy for organizing video content while significantly reducing the computational time and resources required compared to comprehensive analysis.
3Productivity
If extracted video features are compared with stored metadata to incorporate videos into matched categories, then content retrieval efficiency is improved, but system resource consumption increases
Solution Approach 1:
The patent extracts only the essential and discriminative features from video content that are necessary for categorization and retrieval, rather than processing and storing all video data. By taking out only the critical feature elements for comparison against metadata, the system achieves efficient content retrieval while minimizing the energy required for processing and storage.
Data Source
AI summary
In one embodiment, a method including receiving, by a server, an input video and extracting features of the input video to produce extracted features. The method also includes comparing the extracted features of the input video with metadata stored in a metadata database storage and incorporating the input video into a matched video corresponding to metadata that matches the extracted features of the input video upon determining that the extracted features of the input video match metadata in the metadata database storage.