Context-Aware Image Retrieval Using Segmented Neural Networks
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Solution Overview
Problem
Current image retrieval systems are unable to effectively retrieve images of a target object within a specific context, as they lack the capability to disentangle and associate contextual features, leading to inefficient search results for users in online shopping and other applications where visualizing products in different contexts is essential.
Innovation Solution
The system employs neural networks to classify and associate main objects with context objects, allowing for the retrieval of images based on user-specified contexts by identifying common prominent features and grouping images accordingly, leveraging both seller-provided and user-generated content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If existing image-based search techniques use neural networks to recognize objects and generate labels, then images can be retrieved using basic search queries, but the system cannot retrieve images matching particular search attributes over a range of possible options or fine-tune search results using structured search queries
Solution Approach 1:
The patent segments the image analysis process into multiple specialized neural networks: a first neural network for identifying main objects and generating basic labels, and a second neural network for identifying context objects and generating contextual labels. This segmentation allows the system to handle different aspects of image content separately, enabling structured search queries to filter by both main objects and context objects independently, thus achieving fine-tuned search results with preserved contextual information.
Solution Approach 2:
The patent adds a contextual dimension to traditional image search by generating context object labels that describe the environment, accessories, or surrounding elements in images. This dimensional expansion transforms the search space from simple object matching to multi-attribute structured querying, allowing users to search for images with specific combinations of main objects and context objects, thereby achieving versatile search query capabilities without losing contextual information.
2Measurement precision
If image retrieval systems use basic object recognition, then simple image search is possible, but the system cannot identify and retrieve images of objects within a given desired context or disentangle different contextual features
Solution Approach 1:
The patent divides the complex image analysis task into two distinct neural network components: one specialized for main object recognition and another specialized for context object recognition. Each network focuses on a specific aspect of image content, which improves the precision of context identification for each task while managing overall system complexity through modular architecture. The segmented approach allows independent optimization of each network for its specific function.
Solution Approach 2:
The patent creates a multi-functional image retrieval system where the same infrastructure supports both basic object search and contextualized search. The dual neural network architecture enables the system to perform multiple functions: generating basic object labels for simple search queries and generating contextual labels for refined structured queries. This universality allows the system to handle various search complexities without requiring entirely separate systems, thus managing complexity while achieving high measurement precision.
3Quantity of substance
If the system processes and analyzes all available images including user-generated content, then comprehensive image databases are created, but the time and computational resources required for analysis increase significantly
Solution Approach 1:
The patent implements parallel processing through segmented neural networks that operate simultaneously on different aspects of the same image dataset. The first neural network processes images for main object identification while the second neural network processes images for context object identification in parallel. This segmentation of processing tasks reduces overall computation time compared to sequential processing, allowing the system to build comprehensive image databases from large quantities of user-generated content more efficiently.
Solution Approach 2:
The patent performs preliminary action by pre-processing images through the first neural network to generate basic object labels before applying the second neural network for contextual analysis. This staged approach allows the system to quickly filter and organize images by main objects first, then apply more computationally intensive context analysis only where needed, reducing overall processing time while maintaining comprehensive database construction.
Data Source
AI summary
Techniques are provided herein for retrieving images that correspond to a target subject matter within a target context. Although useful in a number of applications, the techniques provided herein are particularly useful in contextual product association and visualization. A method is provided to apply product images to a neural network. The neural network is configured to classify the products in the images. The images are associated with a context representing the combination of classified products in the images. These techniques leverage both seller-provided images of products and user-generated content, which potentially includes hundreds or thousands of images of the same or similar products as the seller-provided images. A graphical user interface is configured to permit a user to select the context of interest in which to visualize the products.