Visual Content Analysis System Semantic Framework
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Solution Overview
Problem
Existing visual content analysis systems struggle to reliably detect and represent dynamic interactions and complex concepts in environments like fulfillment centers and warehouses, as they primarily operate at a low-level descriptor level, lacking a subjective understanding of these environments and the ability to conceptualize interactions between entities.
Innovation Solution
A visual content analysis system that detects and recognizes visual concepts and visual semantics, bridging the gap between human interpretation and low-level descriptors, enabling semantic indexing and concept-based retrieval, and formalizing dynamic interactions through machine learning and metadata tagging.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If manual definition and setting of rules with low-level descriptors is used for event detection, then the system can operate with simple processing, but the ability to discriminate content robustly and reliably at a conceptual level deteriorates
Solution Approach 1:
The patent segments the visual content analysis into multiple hierarchical levels: low-level descriptors (edges, textures, colors), mid-level features (shapes, objects), and high-level semantic concepts (actions, events, relationships). This segmentation allows the system to process information at appropriate levels of abstraction, achieving both computational efficiency and conceptual accuracy.
Solution Approach 2:
The patent introduces a semantic dimension to traditional visual analysis by mapping low-level visual descriptors to high-level semantic concepts through intermediate feature representations. This dimensional transformation enables the system to bridge the gap between pixel-level data and human-interpretable meanings, improving content discrimination without requiring exhaustive manual rule-setting.
2Extent of automation
If generic low-level descriptors are used for visual content analysis, then the system can process data automatically, but the reliability and robustness of content discrimination at a conceptual level deteriorates
Solution Approach 1:
The patent introduces intermediate semantic feature representations that act as mediators between generic low-level descriptors and specific high-level concepts. These intermediate features capture contextual information and relationships, enabling automatic processing to achieve reliable conceptual discrimination without requiring manual intervention at every level.
Solution Approach 2:
The patent dynamically adjusts analysis parameters and feature selection based on the specific context and type of visual content being analyzed. By changing the granularity and focus of feature extraction according to the analysis requirements, the system maintains high reliability across diverse scenarios while preserving automatic processing capabilities.
3Measurement precision
If comprehensive semantic analysis is implemented to improve querying and retrieval accuracy, then the retrieval precision improves, but the computational complexity and processing time increases
Solution Approach 1:
The patent performs preliminary semantic analysis during the video indexing and metadata generation phase, creating pre-computed semantic representations and relationships. This preliminary processing enables fast, accurate querying and retrieval operations without requiring complex real-time computation, as the heavy semantic analysis work is completed in advance.
Data Source
AI summary
A processing device determines a plurality of visual concepts for visual data based on at least one of visual entities in the visual data or feature-level attributes in the visual data, wherein the visual entities are based on the feature-level attributes, and wherein each of the plurality of visual concepts comprises a subject visual entity related to an object visual entity by a predicate. The processing device further determines one or more visual semantics for the visual data based on the plurality of visual concepts, wherein the one or more visual semantics define relationships between the plurality of visual concepts.