Recipe Video Generation Through Step-to-Frame Feature Matching
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Solution Overview
Problem
Existing methods fail to effectively generate comprehensive recipe videos that accurately match cooking steps with corresponding video segments, leading to incomplete or inaccurate cooking instruction videos.
Innovation Solution
A method and device that utilize a machine-learned matching model to correlate text feature information from cooking recipes with image feature information from cooking videos, enabling precise alignment of recipe steps with video segments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing methods are used to generate recipe videos, then the video generation process is simple, but the accuracy of matching cooking steps with video segments is insufficient
Solution Approach 1:
The patent introduces a machine learned matching model as an intermediary component between the cooking recipe text and cooking video frames. This model extracts text features from recipe steps and image features from video frames, then matches them based on feature correlation. The intermediary model resolves the contradiction by providing accurate matching (improving measurement precision) while keeping the overall system structure manageable (controlling device complexity).
Solution Approach 2:
The patent replaces traditional mechanical or rule-based video segmentation methods with a machine learning-based feature matching system. Instead of using fixed temporal boundaries or manual annotation, the system uses neural networks to extract semantic features and match them with corresponding video content. This substitution significantly improves matching accuracy while the modular architecture keeps system complexity acceptable.
2Productivity
If manual annotation is used to match recipe steps with video segments, then matching accuracy is high, but the time and labor required is excessive
Solution Approach 1:
The patent enables the system to automatically perform the matching task that would otherwise require manual annotation. The machine learned matching model processes the cooking recipe and cooking video independently, extracting features and performing matching without human intervention. This self-service capability achieves both high productivity (automated processing) and high matching accuracy (through learned feature correlations).
Solution Approach 2:
The patent transforms the matching problem from a manual temporal boundary definition task to a feature space correlation task. By changing the parameter space from raw video timestamps to extracted text and image features, the system can automatically identify corresponding segments through feature similarity. This parameter transformation enables automated high-accuracy matching without manual labor.
3Manufacturing precision
If the entire cooking video is used for each recipe step, then no additional processing is needed, but the resulting recipe video is redundant and unclear
Solution Approach 1:
The patent applies segmentation by dividing the cooking video into distinct segments that correspond to individual recipe steps. The machine learned matching model identifies precise start and end points for each step by matching text features with image features from video frames. This segmentation creates precise, non-redundant video segments for each cooking action, improving manufacturing precision while the automated matching process keeps processing complexity manageable.
Solution Approach 2:
The patent extracts only the relevant video segments corresponding to each recipe step, removing redundant portions. The matching model identifies and extracts specific time ranges from the full cooking video that correspond to each textual instruction. This extraction process eliminates unnecessary content while the systematic approach to identification keeps the processing complexity controlled.
Data Source
AI summary
A device and method for providing a recipe video are provided, the method including obtaining text feature information corresponding to each step of a cooking recipe, the cooking recipe comprising a plurality of steps corresponding to a plurality of coking actions; obtaining image feature information corresponding to each frame of a plurality of frames constituting a cooking video; matching each step of the cooking recipe with a respective section of the cooking video based on a correlation between the obtained text feature information and the obtained image feature information obtained using a machine learned matching model, wherein the respective section of the cooking video corresponds to a respective step among the plurality of steps; and generating the recipe video by using the respective section of the cooking video matched with each step of the cooking recipe.