Video Identification via Temporal Pattern Signatures
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Solution Overview
Problem
Existing video identification methods are inefficient in detecting near-duplicate videos due to sensitivity to editing operations, high computational costs, and memory requirements, particularly in consumer electronics environments.
Innovation Solution
A method and apparatus that process image sequences by generating descriptor elements for pixel neighborhoods, forming words from these elements, and comparing their frequency and temporal order to identify matching frames, providing a compact representation and robustness to common editing operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If key frames and key-point feature extraction are used for near-duplicate detection, then matching accuracy is improved, but computational cost and storage requirements increase significantly
Solution Approach 1:
The video sequence is divided into shot segments based on detected shot boundaries. Each shot is processed independently to generate a signature, enabling efficient comparison without analyzing entire video sequences. This segmentation reduces computational complexity while maintaining matching accuracy through localized feature analysis.
Solution Approach 2:
The invention extracts only the essential temporal pattern information from video shots to form compact signatures. By extracting and comparing only the critical temporal relationships rather than full feature sets, the method achieves accurate near-duplicate detection with significantly reduced computational cost and storage requirements.
2Quantity of substance
If shot cuts and boundaries are used to form video signatures, then storage requirements are reduced, but detection reliability deteriorates on short sequences and becomes sensitive to shot-detection algorithms
Solution Approach 1:
The method performs preliminary shot boundary detection and segmentation before signature generation. By pre-organizing video content into shot segments with temporal markers, the system creates a robust foundation for subsequent comparison that works reliably on both short and long sequences, reducing sensitivity to variations in shot-detection algorithms.
Solution Approach 2:
The invention changes the parameter representation from simple shot boundary markers to temporal patterns that capture the sequence and duration of shots. This parameter transformation enables reliable detection on short sequences by capturing essential temporal relationships while maintaining compact storage through efficient pattern encoding.
3Productivity
If visual vocabulary clustering is used for feature matching, then searching speed is improved, but the method over-fits to training data and fails to generalise
Solution Approach 1:
Instead of using learned visual vocabularies that may over-fit training data, the invention uses a fixed, predefined set of temporal pattern templates that are copied and applied universally across different video datasets. This approach eliminates over-fitting while maintaining fast searching through efficient template matching, improving generalisation capability without sacrificing speed.
4Productivity
If hash tables are used for fast searching in near-duplicate detection, then searching speed is improved, but memory requirements become prohibitively high for consumer electronics environments
Solution Approach 1:
The video database is segmented into shot-based units with compact temporal signatures. This segmentation enables efficient indexing and searching without requiring large hash tables, as the segmented structure itself provides natural organization for fast retrieval. Consumer electronics devices can store and search these compact signatures with limited memory resources.
Solution Approach 2:
The invention changes the representation parameters from high-dimensional feature vectors requiring large hash tables to compact temporal pattern signatures. This parameter transformation dramatically reduces memory requirements while maintaining fast searching capability through efficient pattern matching algorithms suitable for consumer electronics environments.
Data Source
AI summary
A method and apparatus for processing a first sequence of images and a second sequence of images to compare the first and second sequences is disclosed. Each of a plurality of the images in the first sequence and each of a plurality of the images in the second sequence is processed by (i) processing the image data for each of a plurality of pixel neighborhoods in the image to generate at least one respective descriptor element for each of the pixel neighborhoods, each descriptor element comprising one or more bits; and (ii) forming a plurality of words from the descriptor elements of the image such that each word comprises a unique combination of descriptor element bits. The words for the second sequence are generated from the same respective combinations of descriptor element bits as the words for the first sequence. Processing is performed to compare the first and second sequences by comparing the words generated for the plurality of images in the first sequences with the words generated for the plurality of images in the second sequence.