Video Editing Material Retrieval via Environment Classification
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Solution Overview
Problem
Users face inefficiency and high data flow consumption when selecting video editing materials from a large dataset on mobile terminals, as they need to browse and filter through numerous options to find suitable materials for current video editing tasks.
Innovation Solution
An intelligent mobile terminal uses a convolutional neural network environment classification model to analyze frame images from videos and retrieve video editing materials that match the detected environment, reducing the need for extensive browsing by using classification information as retrieval keywords.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If users browse and filter through a large dataset of video editing materials, then they can find suitable materials for current video editing tasks, but the process is inefficient and consumes high data flow
Solution Approach 1:
The system performs preliminary action by using the environment classification model to pre-analyze the video content and pre-retrieve matching editing materials before the user needs to browse. The model processes the video frames to extract environmental information and automatically filters materials in advance, so when the user views the results, only relevant materials are presented, significantly reducing browsing time and improving efficiency.
Solution Approach 2:
The environment classification model serves as an intermediary between the user's video content and the large database of editing materials. Instead of requiring users to manually search through all materials, the model acts as a mediator that understands the environmental context of the video and translates it into appropriate material recommendations, thereby reducing the time and effort users would otherwise spend filtering through irrelevant options.
2Reliability
If users manually select video editing materials from a large dataset, then they can choose appropriate materials, but data flow consumption increases
Solution Approach 1:
The system extracts only the essential environmental information from the video using the classification model, and then uses this extracted information to retrieve a filtered set of materials. This extraction approach ensures that users receive high-accuracy material recommendations tailored to the specific environmental context of their video, while significantly reducing the amount of data that needs to be processed and displayed, thereby lowering data flow consumption.
Solution Approach 2:
The system changes the parameter of material selection from manual browsing based on multiple criteria to automated retrieval based on environmental classification. By transforming the selection process into a parameter-driven approach where materials are retrieved according to their match with the classified environmental features, the system maintains high selection accuracy while reducing the data flow required for presentation and user review.
Data Source
AI summary
Disclosed is a method of pushing video editing materials including: acquiring an editing instruction; acquiring at least one frame image of an editing video according to the editing instruction, inputting the frame image into an environment classification model, and acquiring classification information which is output by the environment classification model and represents an environment in the frame image; and acquiring video editing materials according to the classification information, so that the video editing materials match an image environment of the frame image. An intelligent mobile terminal is also disclosed.