Video Content Indexing via Feature Clustering and Binary Signatures
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing video content search systems are inefficient and costly in terms of computing resources, and often slow and inaccurate due to the complexity of comparing large video data sets with potential temporal displacements and manipulations.
Innovation Solution
A system that indexes video content by identifying global and local features from each frame, clustering local features, converting them into binary signatures, and generating an index to map relationships, allowing for efficient comparison of query videos against indexed content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional video search methods are used to compare video content, then search accuracy can be maintained, but computing resource consumption increases and search speed decreases
Solution Approach 1:
The patent segments video content into discrete frames and extracts key features (such as SIFT features) from each frame. This segmentation allows the system to process and compare only essential visual elements rather than entire video streams, thereby maintaining search accuracy while significantly reducing computational requirements and improving search speed.
Solution Approach 2:
The patent extracts salient features (keypoints, descriptors) from video frames and creates a condensed representation of video content. By taking out only the most discriminative visual features and storing them in an index, the system enables fast comparison operations that maintain accuracy while reducing the amount of data that needs to be processed during search operations.
2Reliability
If conventional video search methods are used to compare video content, then comprehensive comparison can be performed, but computing resource consumption increases
Solution Approach 1:
The patent performs preliminary processing of video content by extracting features, clustering them, and building an index structure before actual search operations. This preliminary action organizes the data in advance, allowing subsequent comparisons to be performed efficiently with minimal computing resources while maintaining comprehensive comparison capabilities through the structured index.
Solution Approach 2:
The patent transforms video data from raw pixel information into feature space representations (SIFT features, descriptors). This parameter change converts the problem from comparing large amounts of raw visual data to comparing compact feature vectors, significantly reducing computing resource consumption while preserving the ability to perform comprehensive comparisons through the enriched feature representation.
3Measurement precision
If detailed feature extraction is performed on video frames, then search accuracy improves, but processing time increases
Solution Approach 1:
The patent creates compact copies of video content in the form of feature descriptors and signature vectors. Instead of processing and comparing entire video frames during search, the system uses these pre-extracted feature copies, which maintain the discriminative power needed for accurate search while dramatically reducing the time required for processing and comparison operations.
Data Source
AI summary
A method of indexing and searching for video content is provided. For each frame of a first plurality of frames, a first global feature and a first plurality of local features may be identified. The first plurality of local features may be clustered around a first plurality of cluster centers. The first plurality of local features may be converted into a first plurality of binary signatures. An index that maps the first plurality of cluster centers and the first plurality of binary signatures to the first plurality of frames may be generated. A search request associated with a second video may be received and its direct and indirect features may be identified. The identified features of the second video may be compared against the index and a candidate video may be selected as a result of the search request.