Cognitive Video Search Indexing via Entity Segmentation
Solution Overview
Problem
Current data search technologies face challenges in efficiently locating specific information within complex and diverse data sources, particularly with formats like video, which are difficult to search due to the difficulty in articulating parameters and finding relevant results.
Innovation Solution
A method involving segmenting videos into temporal shots, cognitively analyzing them using image recognition algorithms to identify image and personal entities, and generating a searchable index that links these entities to their locations within the video timeline, enabling efficient search and retrieval of specific content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual searching is used to locate specific images in video archives, then search accuracy can be maintained, but search time and efficiency deteriorate significantly as archive size increases
Solution Approach 1:
The system performs preliminary cognitive analysis of video content during the indexing phase, extracting and storing semantic information about objects, events, and concepts before search operations occur. This pre-processing enables rapid query response without compromising search accuracy, as the heavy analytical work is completed in advance during index generation.
Solution Approach 2:
The patent introduces a cognitive index as an intermediary layer between raw video data and search queries. This index contains extracted semantic information that mediates between the video archive and user searches, enabling efficient retrieval without requiring direct manual scanning of video content while maintaining accurate results through structured semantic representation.
2Reliability
If video data is stored in its original format, then data integrity is preserved, but searchability and ease of accessing specific information deteriorate
Solution Approach 1:
The system creates a cognitive copy or representation of video content in the form of structured indexes containing extracted semantic information. This copy enables efficient searching and analysis without modifying or compromising the integrity of the original video data, allowing users to query semantic concepts while the original video files remain unchanged and intact.
3Loss of information
If comprehensive video archives are maintained, then information completeness is improved, but the complexity and difficulty of searching through the data increases
Solution Approach 1:
The patent segments video content into discrete semantic units during indexing, organizing information by objects, events, concepts, and temporal relationships. This segmentation transforms comprehensive video archives into structured, queryable units that reduce search complexity while maintaining complete information availability, allowing users to search for specific semantic elements without navigating through entire video collections.
Data Source
AI summary
A method, computer program product, and a system where a processor(s) obtains a video from a user, via a client, and segments the video into temporal shots that comprise a timeline of the video. The processor(s) cognitively analyze the video, by applying an image recognition algorithm to identify image entities in each temporal shot of the video and by applying a data structure comprising a user profile of the user to the temporal shots, to identity personal entities in each temporal shot of the video. The program code generates a search index for the video, utilizing the user entities (image entities and personal entities), where each entry of the search index is a given user entity and a linkage to a given temporal shot and the linkage indicates a location of the given user entity in the timeline of the video.