Spatial-Semantic Digital Image Search via Neural Feature Vectors
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Solution Overview
Problem
Conventional digital search systems fail to accurately identify digital images based on specific spatial configurations of objects, leading to inefficient user searches for visual content with precise spatial arrangements.
Innovation Solution
A spatial-semantic media search system that utilizes a deep learning model to generate representations of semantic and spatial features from query terms and areas, allowing for the identification of digital images with targeted visual content within specific regions through a query neural network and digital image neural network comparison.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional text-based search or similar image search is used, then the search system can identify digital visual media portraying certain content, but the system cannot accurately identify digital images based on spatial arrangement of objects
Solution Approach 1:
The patent segments the search query into multiple components: text queries are divided into individual words or phrases, and image queries are divided into multiple candidate regions. Each segment is processed independently to extract spatial features, which are then combined to form a comprehensive spatial-semantic representation. This segmentation enables the system to capture both content semantics and spatial arrangement separately and accurately.
Solution Approach 2:
The patent introduces a new dimensional representation by combining traditional semantic search dimensions with spatial dimension. Instead of searching only in semantic space or only in pixel space, the system creates a joint spatial-semantic feature space that incorporates both content meaning and spatial arrangement. This dimensional expansion allows simultaneous consideration of what objects are present and where they are located.
2Productivity
If users search for digital images with specific spatial configuration, then the search results can be more precise, but users have to sort through many irrelevant results to find matching images
Solution Approach 1:
The system performs preliminary processing of both the query and database images by pre-extracting spatial-semantic features before the actual search execution. Candidate regions are pre-identified and their spatial relationships are pre-computed and stored. When a search is executed, the system compares these pre-computed features directly, avoiding time-consuming processing during the search phase and significantly reducing the time to retrieve relevant results.
3Measurement precision
If the search system incorporates spatial features, then the search accuracy for spatial arrangement improves, but the system complexity increases
Solution Approach 1:
The patent introduces feature vectors as an intermediary representation that bridges the gap between complex image data and simple comparison operations. Spatial-semantic features are extracted and represented as compact feature vectors that capture both content and spatial information. These vectors serve as intermediaries that enable efficient comparison without requiring complex direct image analysis, thus reducing system complexity while maintaining high accuracy.
4Adaptability or versatility
If conventional search systems are used, then the implementation is simpler, but the system cannot bridge the semantic gap between low-level pixel features and high-level concepts
Solution Approach 1:
The patent replaces traditional mechanical image processing methods with deep learning-based feature extraction. Instead of using hand-crafted features or simple pixel comparisons, the system employs trained neural network models that automatically learn to extract meaningful spatial-semantic features from images. This substitution enables the system to understand high-level concepts and spatial relationships without manual feature engineering, bridging the semantic gap between low-level pixels and high-level meanings.
Data Source
AI summary
The present disclosure includes methods and systems for searching for digital visual media based on semantic and spatial information. In particular, one or more embodiments of the disclosed systems and methods identify digital visual media displaying targeted visual content in a targeted region based on a query term and a query area provide via a digital canvas. Specifically, the disclosed systems and methods can receive user input of a query term and a query area and provide the query term and query area to a query neural network to generate a query feature set. Moreover, the disclosed systems and methods can compare the query feature set to digital visual media feature sets. Further, based on the comparison, the disclosed systems and methods can identify digital visual media portraying targeted visual content corresponding to the query term within a targeted region corresponding to the query area.