Panoramic Semantic Image Analysis via Multi-Subject Relational Vectors
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Solution Overview
Problem
Current image description technologies can only perform low-level semantic descriptions, failing to provide panoramic semantic descriptions that capture relationships between multiple subjects, actions, and actions in images.
Innovation Solution
An image analysis method and system that extracts influencing factors such as location, attribute, and posture features of target subjects, along with relational vector features, using convolutional neural networks (CNN) and recurrent neural networks (RNN) to generate a higher-level panoramic semantic description.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If image description technology performs low-level semantic description on single subject or single action, then the description accuracy for individual elements is improved, but the ability to describe panoramic semantic relationships between multiple subjects and actions deteriorates
Solution Approach 1:
The patent segments the image analysis process into multiple specialized modules: a multi-subject relationship recognition module that extracts relationships between subjects, a multi-action recognition module that extracts actions, and a panoramic semantic description module that integrates these elements. This segmentation allows each module to specialize in specific tasks while collectively achieving comprehensive panoramic semantic description capability.
Solution Approach 2:
The patent transitions from low-level single-subject/single-action description to high-dimensional panoramic semantic description by introducing multiple dimensions of analysis: subject relationships, action relationships, and their interactions. This dimensional expansion enables the system to capture complex semantic structures that cannot be represented in traditional single-dimension description frameworks.
2Measurement precision
If image description technology focuses on detailed analysis of individual subjects and actions, then the precision of individual element recognition is improved, but the computational complexity and processing time increase
Solution Approach 1:
The system divides the complex image analysis task into distinct functional modules: feature extraction, multi-subject relationship recognition, multi-action recognition, and panoramic semantic description generation. Each module processes specific aspects independently, reducing the complexity burden on any single component while maintaining high recognition precision through specialized processing.
Solution Approach 2:
The patent introduces intermediate representation structures that bridge detailed feature extraction and high-level semantic description. These intermediate structures organize extracted features and relationships in a structured format that facilitates both precise recognition and efficient processing, acting as mediators between low-level feature detection and high-level semantic interpretation.
3Adaptability or versatility
If image description technology extracts and processes multiple types of features from multiple frames, then the panoramic semantic description quality is improved, but the calculation amount and processing time increase
Solution Approach 1:
The system performs preliminary feature extraction from multiple image frames before conducting relationship and action analysis. By pre-processing and organizing features in advance, the system reduces the computational burden during the subsequent panoramic semantic description generation phase, improving overall processing efficiency without sacrificing description quality.
Solution Approach 2:
The patent merges the processing of multiple feature types (location, attribute, posture features from multiple frames) into an integrated panoramic semantic description framework. This consolidation allows the system to process diverse features simultaneously through unified relationship and action recognition modules, improving productivity by eliminating redundant processing steps.
Data Source
AI summary
An image analysis method, including: obtaining influencing factors of t frames of images, where the influencing factors include self-owned features of h target subjects in each of the t frames of images and relational vector features between the h target subjects in each of the t frames of images, self-owned features of each target subject include a location feature, an attribute feature, and a posture feature, and t and h are natural numbers greater than 1; and obtaining a panoramic semantic description based on the influencing factors, where the panoramic semantic description includes a description of relationships between target subjects, relationships between actions of the target subjects and the target subjects, and relationships between the actions of the target subjects.