Context-Aided Visual Search Using Scene Classification and User Input
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Solution Overview
Problem
Existing computer-vision object detection and localization approaches face challenges with limited training data sets, leading to reduced accuracy and increased computational costs, especially when dealing with large ontologies of objects and complex scenes, and often fail to align with user intent due to ambiguity in image processing.
Innovation Solution
The proposed solution involves a context-aided visual search system that uses a trained context classification model to enrich feature sets by identifying image contexts, such as scenes and geographical information, and integrates user input location to refine object recognition, allowing for more accurate and efficient detection of objects with smaller training sets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional object detection models are trained with large training data sets to improve accuracy, then detection precision improves, but computational costs and training time increase significantly
Solution Approach 1:
The patent segments the object detection task into two distinct stages: (1) context classification that identifies the scene type and environmental conditions, and (2) object detection that operates within the constrained context. This segmentation allows each component to be trained more efficiently with smaller, targeted data sets rather than requiring exhaustive training data for all possible objects and scenes.
Solution Approach 2:
The context classification model performs preliminary action by pre-processing the input image to determine the scene context before object detection begins. This preliminary classification provides prior information that constrains the subsequent object detection search space, effectively reducing the computational burden and training data requirements for the object detection model.
2Reliability
If computer vision models process entire images to detect objects, then comprehensive object detection is achieved, but computational resources and processing time increase
Solution Approach 1:
The patent segments the image processing workload by first classifying the context to identify relevant scene characteristics, then using this information to guide object detection only in relevant regions or with constrained object categories. This avoids processing the entire image at full resolution for all possible object types simultaneously.
Solution Approach 2:
Context classification serves as a preliminary filtering step that identifies which objects are likely to be present based on scene type. This allows the system to focus computational resources on detecting only those object categories relevant to the identified context, rather than searching for all possible objects throughout the entire image.
3Speed
If object detection is performed without context information, then processing speed is maintained, but accuracy decreases in ambiguous or complex scenes
Solution Approach 1:
The context classification model performs a quick preliminary analysis of the scene to extract contextual information before object detection begins. This preliminary step operates at lower computational cost and provides guiding information that accelerates the subsequent object detection process by constraining the search space, thereby maintaining speed while improving accuracy.
Solution Approach 2:
Context information acts as an intermediary between the raw image input and the object detection process. The context classification model extracts this intermediate representation that bridges the gap between speed and accuracy by providing scene-level understanding that guides more accurate object detection without requiring exhaustive processing.
4Adaptability or versatility
If large training data sets are used to cover diverse object ontologies, then object recognition coverage improves, but data storage requirements and model complexity increase
Solution Approach 1:
The patent segments the knowledge representation into two parts: a context ontology that captures scene-level information and an object ontology that captures object-level information. This segmentation allows the system to achieve comprehensive object ontology coverage by combining context-aware scene understanding with object detection, rather than requiring a single monolithic model to learn all objects in all possible contexts.
Solution Approach 2:
Context classification performs preliminary categorization of the scene type, which constrains the object detection to a subset of relevant object categories. This approach achieves versatile object recognition coverage by adapting the object search space based on context, rather than requiring the model to be trained on and capable of detecting all possible objects equally well.
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.