Video Descriptor Extraction via Key Frame Segmentation
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Solution Overview
Problem
Current video search methods are inefficient in reducing temporal redundancy and predicting areas of interest in video content, failing to fully analyze video content semantically, such as locating faces, due to their inability to account for video dynamism and complex language constructs.
Innovation Solution
A method and apparatus that extracts semantic descriptors from video content using a Key Frame Extractor, Content Analyser, Descriptors Extractor, and Temporal Coding block, selecting key frames based on visual homogeneity, classifying image patches for descriptor extraction, and multiplexing temporal information to generate semantic descriptors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If full frame processing is applied to all video frames, then descriptor extraction completeness is improved, but processing time and computational complexity increase significantly due to temporal redundancy
Solution Approach 1:
The video sequence is segmented into key frames and non-key frames based on temporal redundancy analysis. Only key frames undergo full descriptor extraction processing, while non-key frames are skipped or processed minimally. This segmentation resolves the contradiction by dividing the processing workload strategically, maintaining descriptor completeness for important frames while reducing overall processing time through selective processing.
2Adaptability or versatility
If statistical features including distance from image centre are used for key point selection, then adaptivity to input data is improved, but natural characteristics of video content such as faces and semantic regions are not captured
Solution Approach 1:
A content analysis block is introduced as an intermediary between the key frame extractor and the descriptor extraction process. This intermediary analyzes semantic characteristics of key frames (such as detecting faces, text, or important objects) and uses this analysis to guide the selection of regions for descriptor extraction. This resolves the contradiction by adding a mediating layer that combines statistical adaptivity with semantic understanding, ensuring both data adaptivity and accurate capture of natural video characteristics.
3Adaptability or versatility
If video transformations such as cropping and motion warping are applied, then delivery adaptability is improved, but consistency of semantic descriptors across transformations is compromised
Solution Approach 1:
The system performs preliminary analysis of the video content to identify semantically important regions and objects before delivery transformations are applied. Descriptors are extracted and registered based on these pre-identified semantic regions. When transformations like cropping or motion warping are subsequently applied during delivery, the descriptor registration system can compensate for these transformations by referencing the preliminary semantic analysis, thereby maintaining descriptor consistency across different delivery conditions while preserving delivery adaptability.
Data Source
AI summary
A method for extraction of descriptors from video content, includes the following steps: a Key Frame Extracting step, applying a local descriptors-based approach to select pictures of the incoming video as key frames that are representative of a temporal region of the video which is visually homogeneous; a Content Analysis step, analysing the content of the key frames and classifying image patches of the key frames as interesting or not for the extraction of descriptors; a Descriptors Extracting step, extracting compact descriptors from the selected key frames, and defining a set of surrounding images also on the basis of input received from the Content Analysis step; a Temporal Coding step, multiplexing information about the time points at which said key frames have been extracted in the Key Frame Extracting step with the compact descriptors extracted in the Descriptors Extracting step, obtaining the descriptors.