Depth-Based Image Tagging for Spatial Metadata
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Solution Overview
Problem
Image searches are inefficient due to the lack of depth information in metadata, leading to irrelevant or unusable search results, as existing metadata does not effectively indicate the spatial relationships and positions of objects within images, causing users to spend excessive time finding relevant images.
Innovation Solution
An automated method using machine-learning models and image processing techniques to generate metadata that includes depth information, allowing for the detection of objects and estimation of their spatial relationships, thereby enhancing the relevance and efficiency of image search results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If basic manual or automated image tagging techniques are used, then metadata generation is simple and fast, but the metadata lacks depth information and spatial relationship details
Solution Approach 1:
The patent transitions from traditional 2D image tagging to 3D spatial-aware tagging by incorporating depth information. The system generates metadata that includes depth values and spatial relationships between objects, effectively adding a third dimension to the traditional image tagging approach. This enables the metadata to represent not just what objects are present, but also their spatial positioning and relative depths in the scene.
Solution Approach 2:
The patent segments the image processing task into multiple independent modules: object detection, depth estimation, and metadata generation. Each module processes specific aspects of the image separately before integrating the results into comprehensive metadata. This segmentation allows complex depth-aware tagging to be achieved through composition of simpler, specialized functions.
2Reliability
If depth-based metadata generation is implemented, then search relevance is improved, but computational resources and processing time increase
Solution Approach 1:
The patent performs depth estimation and spatial relationship analysis during the initial image processing stage, before the actual search query is received. By pre-computing and storing depth-based metadata features, the system prepares the data in advance, so that when search queries arrive, the relevant information is already available for quick retrieval without requiring real-time computational processing.
Solution Approach 2:
The patent applies different processing intensities to different regions and objects within images based on their importance. Rather than uniformly processing all image data at maximum complexity, the system identifies key objects and regions that are most likely to be relevant to search queries and applies enhanced depth analysis only to those specific areas, while using simpler processing for less critical regions.
3Productivity
If existing metadata without depth information is used, then the system is simple and fast, but users must manually review many irrelevant images
Solution Approach 1:
The patent incorporates feedback mechanisms where the system continuously learns from user interactions with search results. When users view or select images, the system uses this feedback to refine its depth-based metadata generation and object detection algorithms. This feedback loop enables the system to improve its spatial understanding and tagging accuracy over time, making subsequent searches more efficient and reducing the need for manual review of irrelevant images.
Data Source
AI summary
Systems, methods, devices, and non-transitory computer-readable media for depth-based image tag generation are described. The disclosed technology may access images stored in an image repository. The images may comprise a first image. Using image processing techniques, a first object and a second object in the first image may be detected. Using machine-learning models, a first tag associated with the first object and a second tag associated with the second object may be identified. Using the machine-learning models, a first depth value associated with the first object and a second depth value associated with the second object may be determined. Based on the first depth value and the second depth value, a spatial relationship between the first object and the second object may be determined. Metadata associated with the first image may be generated. The metadata may indicate the spatial relationship between the first object and the second object.