Object Classification via Semantic Mapping and Projection
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Solution Overview
Problem
Existing object classification techniques lack a unified semantic solution that invites user interaction and produces human-readable outputs, making it challenging to effectively classify objects in a continuous space.
Innovation Solution
The proposed system employs semantic mapping by transforming image data into directed quantities expressed in terms of semantic parameters, projecting them onto an object representation map, and associating objects with categories based on semantic similarity, supercategories, and attributes, providing human-readable outputs and adaptive revision of the classification map.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If traditional object classification techniques are used, then classification can be performed, but the output is not human-readable and user interaction is not invited
Solution Approach 1:
The patent introduces semantic maps and semantic parameters as intermediary representations between image data and classification results. These semantic maps serve as a bridge that translates visual information into human-readable semantic descriptions, enabling both machine processing and human interpretation simultaneously
Solution Approach 2:
The system implements interactive feedback loops where users can provide corrections or refinements to the semantic classification results. The system incorporates this user feedback to iteratively improve the classification accuracy and adjust semantic parameters, making the classification process adaptive and user-guided
2Loss of information
If a unified semantic solution is implemented, then human-readable output and user interaction are enabled, but system complexity increases
Solution Approach 1:
The patent segments the classification system into distinct modular components: image processing modules, semantic map generation modules, parameter extraction modules, and user interaction modules. This segmentation allows each component to be developed and optimized independently, managing overall system complexity through modular architecture
Solution Approach 2:
The semantic maps serve multiple functions simultaneously: they represent object categories, encode semantic relationships, provide human-readable descriptions, and enable user interaction. This multi-functionality reduces the need for separate specialized systems, thereby managing complexity while achieving unified semantic processing
Data Source
AI summary
There are provided systems and methods for performing object classification through semantic mapping. Such an object classification system includes a system processor, a system memory, and an object categorizing unit stored in the system memory. The system processor is configured to execute the object categorizing unit to receive image data corresponding to an object, and to transform the image data into a directed quantity expressed at least in part in terms of semantic parameters. The system processor is further configured to determine a projection of the directed quantity onto an object representation map including multiple object categories, and to associate the object with a category from among the multiple object categories based on the projection.