Image Descriptor Extraction for Video Shot Transition Detection
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Solution Overview
Problem
Existing methods for detecting shot transitions in video sequences are either spatially insensitive to color content, overly sensitive to motion, or computationally expensive, leading to inefficiencies and information loss.
Innovation Solution
The method extracts image descriptors that capture spatially sensitive color content and interrelation information uniformly across image planes and multiple scales, reducing computational load and allowing perfect reconstruction, while being more robust to noise and motion.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If histogram-based color difference is used for shot transition detection, then color content information is captured, but spatial arrangement information is lost
Solution Approach 1:
The image is divided into multiple blocks, and each block is processed independently to extract color and spatial features. This segmentation allows the method to capture both color content and spatial arrangement information without losing the richness of the original image data.
Solution Approach 2:
The patent transforms the 2D spatial arrangement information into a 1D ordered sequence based on block positions, and combines it with color histogram features to create a multi-dimensional feature vector. This dimensional transformation preserves spatial information while enabling efficient comparison.
2Loss of information
If edge map-based spatial interrelation is used for shot transition detection, then spatial information is captured, but color content information is lost
Solution Approach 1:
The patent merges color histogram features (capturing color content) with spatial arrangement features (capturing spatial interrelations) into a unified feature vector. This combination ensures that both color and spatial information are utilized simultaneously for shot transition detection.
3Reliability
If pixel-wise difference is used for shot transition detection, then spatial sensitivity is achieved, but noise and motion sensitivity increases
Solution Approach 1:
The patent extracts dominant color features from each block using histogram analysis, which filters out noise and minor variations. By working with aggregated color statistics rather than individual pixel values, the method becomes more robust to noise and motion while maintaining spatial sensitivity through block-based processing.
4Reliability
If edge detection and image registration are used to counteract motion sensitivity, then motion robustness is improved, but computational expense increases
Solution Approach 1:
The patent uses motion-compensated block matching that adapts to the actual motion in the video sequence. By dynamically adjusting the search window and using hierarchical block matching, the method achieves motion robustness while significantly reducing computational expense compared to full-frame registration.
5Measurement precision
If block-based difference with multiple thresholds is used for shot transition detection, then detection accuracy is improved, but computational complexity increases
Solution Approach 1:
The patent performs preliminary block-based color histogram extraction and spatial arrangement encoding before comparing frames. By pre-processing and organizing the feature data in advance, the method reduces the computational complexity of frame comparison while maintaining high detection accuracy through the use of multiple thresholds.
Data Source
AI summary
A method of representing at least one image comprises deriving at least one descriptor based on color information and color interrelation information for at least one region of the image, the descriptor having at least one descriptor element, derived using values of pixels in said region, wherein at least one descriptor element for a region is derived using a non-wavelet transform. The representations may be used for image comparisons.