Object-Based Music Clip Generation via Inter-Object Relationship Scoring
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Solution Overview
Problem
The process of creating a compelling music clip from a collection of images is time-consuming and requires editing knowledge, particularly in arranging images to tell a story, as existing methods lack efficient computer-based decision-making for ordering and arrangement.
Innovation Solution
An object-based editing system that utilizes a cost function, known as the 'inter-object relationships' score, to optimize the arrangement of images in both space and time, considering detected objects and framing options, to create a narrative sequence, with optional modules for photo analysis, story-telling optimization, and production, including transitions and effects synchronization with music.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual editing and arrangement of images is used to create music clips, then storytelling quality can be improved, but time consumption and complexity increase
Solution Approach 1:
The system performs automatic object detection, relationship scoring, and image arrangement without requiring manual user intervention. The computer vision system independently analyzes images, identifies objects, calculates inter-object relationship scores, and generates the final music clip sequence, enabling the system to serve itself rather than requiring human editors to manually curate and arrange images.
Solution Approach 2:
The patent replaces manual mechanical editing operations with automated computer vision and machine learning systems. Instead of human editors visually inspecting and arranging images, the system uses deep learning models to detect objects, compute relationships between objects, and automatically determine the optimal sequence for the music clip, substituting human cognitive and manual tasks with automated computational processes.
2Productivity
If automated computer-based decision making is used for image ordering, then time consumption is reduced, but decision-making accuracy and storytelling quality may deteriorate
Solution Approach 1:
The system incorporates feedback mechanisms where the inter-object relationship scores are calculated based on detected object relationships, and this scoring feedback is used to iteratively optimize the image arrangement. The system continuously refines the sequence based on the calculated relationships between objects, ensuring that the automated decisions align with coherent storytelling principles by using the relationship scores as feedback for optimization.
Solution Approach 2:
The patent changes the parameters for image arrangement from traditional manual selection criteria to automated parameters such as inter-object relationship scores, object detection confidence, and spatial-temporal consistency metrics. By transforming the decision-making parameters into quantifiable metrics that can be processed by machine learning models, the system achieves both speed and accuracy in generating story-coherent sequences.
3Device complexity
If simple image sequencing is used, then processing complexity is reduced, but ability to tell a coherent story deteriorates
Solution Approach 1:
The system segments the image collection into distinct objects and relationships through computer vision analysis. Each image is analyzed to identify individual objects, and then the system creates a segmented representation of object relationships across multiple images. This segmentation allows the complex task of creating coherent storytelling to be broken down into manageable steps: object detection, relationship identification, and sequence optimization based on these segmented relationships.
Solution Approach 2:
The patent adds new dimensions to the image arrangement problem by incorporating inter-object relationship scores and spatio-temporal considerations. Instead of simply sequencing images based on time or randomness, the system adds relationship-based dimensions and object interaction dimensions to the arrangement criteria, enabling coherent storytelling through multi-dimensional optimization of image sequences.
Data Source
AI summary
A method and a system for automatic generation of clips from a plurality of images based on inter-object relationships score are provided herein. The method may include: obtaining a plurality of images, wherein at least two of the images contain at least one object over a background; analyzing at least some of the images to detect objects; extracting geometrical meta-data of at least some of the detected objects; calculating an inter-object relationships score for at least some of the detected objects; and determining a spatio-temporal arrangement of at least some of the objects and at least some of the images based at least partially on the inter-object relationships score and the geometrical meta-data of at least some of the detected objects.