Image-Sound Segment Correspondence via Temporal Co-presence
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Solution Overview
Problem
Existing methods for finding correspondence between image segments and sound segments often incorrectly match multiple image segments to the same sound segment due to lack of consideration for correlation between occurrence patterns and time co-presence, leading to erroneous matches between image segments and sound segments that appear close in time but contain different objects.
Innovation Solution
An apparatus and method that classify image and sound segments into groups based on shared objects, calculate similarity scores based on time duration of co-presence, and decide correspondence using these scores to ensure accurate matching of segments with identical objects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If correspondence is made based on occurrence patterns of image and sound segments, then matching speed is improved, but matching accuracy deteriorates due to erroneous matches
Solution Approach 1:
The patent segments both image data and sound data into discrete segments based on temporal boundaries. Image segments are created by dividing the video timeline, and sound segments are created by dividing the audio timeline. This segmentation allows for precise temporal alignment and correspondence analysis, enabling the system to match segments that occur simultaneously or in close proximity, thereby improving both speed and accuracy of matching.
Solution Approach 2:
The patent introduces a temporal dimension to the correspondence problem by analyzing the time positions and durations of image and sound segments. Instead of merely comparing content similarity, the system incorporates temporal information (start time, end time, duration) as an additional dimension for matching. This temporal dimension allows the system to distinguish between segments that occur at different times, preventing erroneous matches while maintaining high matching speed through efficient temporal indexing.
2Productivity
If multiple image segments with similar occurrence patterns are matched to the same sound segment, then processing efficiency is improved, but reliability deteriorates due to erroneous correspondence
Solution Approach 1:
The patent applies local quality by analyzing the specific temporal characteristics of each image-sound segment pair rather than applying a uniform matching rule to all segments. Each segment pair is evaluated based on its unique temporal properties (overlap duration, temporal proximity, synchronization point), allowing the system to make locally optimal matching decisions. This enables the system to efficiently process multiple segments while maintaining high reliability by avoiding erroneous correspondences that would result from uniform matching approaches.
Solution Approach 2:
The patent incorporates feedback mechanisms through iterative refinement of segment correspondence. The system initially performs rapid matching based on temporal patterns, then uses feedback from analysis of segment content and temporal relationships to refine and correct erroneous matches. This feedback loop allows the system to maintain processing efficiency while improving reliability by correcting mismatches that would otherwise propagate through the entire segmentation.
3Device complexity
If segments are matched without considering time co-presence, then device complexity is reduced, but measurement precision deteriorates
Solution Approach 1:
The patent performs preliminary action by pre-processing the video and audio streams to create segmented representations with associated temporal metadata before the actual correspondence analysis. Image segments are pre-divided with start/end time information, and sound segments are pre-segmented with temporal boundaries. This preliminary segmentation and temporal annotation enables the matching system to efficiently consider time co-presence without adding complex real-time processing, thereby maintaining low device complexity while achieving high correspondence precision.
Data Source
AI summary
An apparatus includes an image segment classification means that analyzes an input video to generate image segment groups each segment including image segments which include an identical object; a sound segment classification means that analyzes the input video to generate sound segment groups each segment including sound segments which include an identical object; an inter-segment group score calculation means that calculates a similarity score between each image segment group and each sound segment group; and a segment group correspondence decision means that decides, using the scores, whether or not an object in the image segment groups and an object in the sound segment groups are the same.