Context-Aided Visual Search Using Scene Classification
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing computer vision systems face challenges with object detection and localization, particularly when dealing with limited training data sets and large ontologies, leading to reduced accuracy and increased computational costs, as well as misalignment with user intent due to insufficient contextual information.
Innovation Solution
The integration of a context classification model with an object recognition model to enrich feature sets by classifying image contexts, such as scenes and user inputs, which helps in narrowing the search space and improving detection accuracy by weighting features based on context, and leveraging user inputs like touch locations to refine object recognition.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional object detection models are used with large ontologies, then object detection coverage is improved, but computational costs and processing time increase significantly
Solution Approach 1:
The patent segments the object detection process into two distinct stages: (1) context classification that identifies the scene type and relevant object categories, and (2) focused object detection that only searches for objects within the identified context. This segmentation allows the system to handle large ontologies efficiently by dividing the comprehensive object search into manageable context-specific sub-tasks, reducing overall processing time while maintaining detection coverage across diverse object types.
Solution Approach 2:
The patent applies preliminary action by performing context classification before object detection. The context classification model pre-identifies the scene type (e.g., kitchen, bedroom, outdoor) and determines which object categories are relevant to that context. This preliminary step filters out irrelevant objects from the ontology before the main detection process, allowing the system to prepare and focus computational resources on only the necessary object categories for the given scene, thereby reducing processing time without sacrificing detection coverage.
2Measurement precision
If comprehensive object detection is performed without context, then object detection accuracy is improved, but computational resources required increase
Solution Approach 1:
The patent applies local quality by making the object detection process context-dependent. Instead of uniformly applying high-computation detection across all possible objects in all scenes, the system applies detection accuracy selectively based on the identified context. For each context type, the system focuses computational resources on detecting only the object categories relevant to that context with high accuracy, while ignoring irrelevant objects. This localized approach maintains detection accuracy for relevant objects while significantly reducing overall computational resource consumption.
Solution Approach 2:
The patent changes the parameter of object category relevance based on context classification. The system dynamically adjusts which object categories from the ontology are active for detection based on the identified scene context. This parameter change allows the system to switch between different detection configurations optimized for different contexts (e.g., food items for kitchen scenes, furniture for bedroom scenes), maintaining high detection accuracy for relevant objects while reducing computational resources by deactivating detection for irrelevant object categories in each specific context.
3Device complexity
If context information is not utilized, then system complexity is reduced, but object detection accuracy in specific scenes decreases
Solution Approach 1:
The patent segments the system into two specialized components: a context classification model and an object detection model. The context classification model handles the complex task of scene understanding and context identification, while the object detection model focuses on accurate object recognition within the identified context. This segmentation allows each component to be optimized for its specific function, maintaining high object detection accuracy through context awareness while managing system complexity by separating concerns into modular, independently optimizable units.
Solution Approach 2:
The patent introduces context classification as an intermediary layer between the input image and the object detection process. This intermediary component analyzes the scene to identify relevant context information (scene type, lighting conditions, dominant objects) and uses this information to guide the object detection process. The context intermediary enables the system to achieve high detection accuracy by providing relevant contextual cues without requiring the entire complex ontology to be processed, thus balancing the need for accuracy with manageable system complexity through the mediating context layer.
Data Source
AI summary
Provided is a technique for determining a context of an image and an object depicted by the image based on the context. A trained context classification model may determine a context of an image, and a trained object recognition model may determine an object depicted by the image based on the image and the context. Provided is also a technique for determining an object depicted within an image based on an input location of an input detected by a display screen. An object depicted within an image may be detected based on a distance in feature space between an image feature vector of an image and a feature vector of the object, and a distance in pixel-space between an input location of an input and location of the object within the image.