Visual Recognition Video Frame Clustering for Resource Reduction
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Solution Overview
Problem
Current visual recognition techniques for video streams are resource-intensive due to the need to process all video frames, which is costly and inefficient, especially with the growing volume of video data online.
Innovation Solution
Implement a method that coarsely segments video frames into clusters based on image similarity, samples frames with maximum dissimilarity, determines cluster quality, and iteratively re-clusters to improve quality, reducing the number of frames that need to be analyzed through the use of Max Distance Cluster Tiling (MDCT) and greedy cluster aggregation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If all video frames are processed for visual recognition, then recognition accuracy is improved, but resource utilization increases significantly
Solution Approach 1:
The video stream is segmented into multiple clusters based on scene similarity, where frames within each cluster share comparable visual characteristics. This segmentation allows the system to process only representative frames from each cluster rather than all frames, thereby reducing resource utilization while maintaining recognition accuracy through adequate sampling of diverse scene types.
Solution Approach 2:
Instead of processing every original video frame, the system creates a representative subset by selecting sample frames from each cluster. These sampled frames serve as copies that capture the essential visual information of their respective clusters, enabling accurate visual recognition with significantly reduced computational resources.
2Quantity of substance
If video frames are sampled at frame-per-second rate, then more frames are available for analysis, but resource costs increase by up to 60% compared to clustered sampling
Solution Approach 1:
The video stream is divided into clusters of frames with similar visual content. By processing only representative samples from each cluster rather than all frames at frame-per-second rate, the system achieves effective visual recognition coverage while reducing the quantity of frames analyzed and the associated resource costs by up to 60%.
Solution Approach 2:
The system performs partial action by selecting a sufficient subset of frames from each cluster for visual recognition processing. This partial sampling approach provides adequate representation of the video content without the excessive resource consumption of processing every frame at maximum rate.
3Productivity
If video frames are sampled at frame-per-shot rate, then fewer frames are processed, but resource costs still increase by up to 20% compared to clustered sampling
Solution Approach 1:
The system segments frames into clusters based on fine-grained scene similarity, creating more granular groups than traditional shot-based segmentation. This finer segmentation enables more precise sampling strategies that reduce the number of frames requiring processing while maintaining comprehensive scene coverage, thereby improving productivity and reducing resource costs by up to 20% compared to frame-per-shot sampling.
Solution Approach 2:
The system changes the segmentation parameter from shot-based boundaries to similarity-based clustering, creating clusters that more accurately reflect actual visual content groupings. This parameter change optimizes the sampling process by ensuring that selected frames represent distinct visual scenarios, improving processing efficiency while minimizing resource consumption.
4Loss of information
If clustering is performed based on image similarity, then frames with similar content are grouped, but additional processing steps are required
Solution Approach 1:
The video stream undergoes segmentation into clusters based on image similarity metrics. This segmentation reduces scene redundancy by grouping frames with comparable visual content, allowing the system to process only representative frames from each cluster. The added processing step of similarity-based clustering is offset by the substantial reduction in subsequent visual recognition processing requirements.
Solution Approach 2:
Image similarity-based clustering is performed as a preliminary action before visual recognition processing. This pre-processing step organizes frames into meaningful groups, reducing the volume of data requiring expensive visual recognition analysis. The preliminary clustering action eliminates redundant processing of visually similar frames while preserving all unique scene information.
Data Source
AI summary
Embodiments may provide techniques that provide identification of images that can provide reduced resource utilization due to reduced sampling of video frames for visual recognition. For example, in an embodiment, a method of visual recognition processing may be implemented in a computer system comprising a processor, memory accessible by the processor, and computer program instructions stored in the memory and executable by the processor, the method comprising: coarsely segmenting video frames of video stream into a plurality of clusters based on scenes of the video stream, sampling a plurality of video frames from each cluster; determining a quality of each cluster, re-clustering the video frames of video stream to improve the quality of at least some of the clusters.