N-Dimensional Spatial Indexing for Location-Based Image Retrieval
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Solution Overview
Problem
Current methods for image retrieval from large collections, especially those with location data, are inefficient and lack effective techniques for storing, analyzing, and ranking images based on visibility and relevance.
Innovation Solution
A method using N-dimensional coordinates to retrieve digital images where a point of interest is visible, involving the computation of field of view polytopes and discrete oriented polytopes, along with spatial indexing and ranking algorithms to prioritize images based on visibility and relevance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If traditional image retrieval methods are used on large collections with location data, then comprehensive image coverage is achieved, but retrieval time becomes excessively long (over 10 milliseconds)
Solution Approach 1:
The patent segments the image collection by creating spatial indexes that divide the search space into manageable regions. Images are organized according to their geographic locations and fields of view, allowing the system to search only relevant segments rather than the entire collection, thus reducing retrieval time from over 10 milliseconds to 1-2 milliseconds.
Solution Approach 2:
The patent performs preliminary actions by pre-computing and storing spatial indexes, field of view polytopes, and discrete oriented polytopes for all images during an indexing phase. This preliminary organization of data based on location and visibility information enables rapid query processing without requiring complex computations during actual retrieval operations.
2Measurement precision
If N-dimensional spatial indexing is implemented for precise location-based retrieval, then retrieval accuracy improves, but system complexity increases
Solution Approach 1:
The patent extends traditional spatial indexing by incorporating N-dimensional coordinates that include not only geographic location but also field of view orientation and other spatial parameters. This dimensional expansion allows the system to precisely determine whether points of interest are visible in images by checking if they fall within computed polytopes, achieving high retrieval accuracy while managing complexity through systematic indexing.
Solution Approach 2:
The patent introduces discrete oriented polytopes (DOPs) as intermediary structures that bound the field of view polytopes. These DOPs serve as simplified geometric representations that facilitate efficient point-in-polytope testing during query processing, bridging the gap between complex field of view calculations and practical retrieval operations.
3Measurement precision
If field of view polytopes are computed for all images to ensure accurate visibility determination, then retrieval precision improves, but computational overhead increases
Solution Approach 1:
The patent computes field of view polytopes and their bounding discrete oriented polytopes during an offline indexing phase rather than during online query processing. This preliminary computation transforms complex geometric calculations into pre-stored data structures, enabling rapid visibility determination during retrieval by simply checking whether query points fall within the pre-computed polytopes, thus minimizing real-time computational overhead.
Data Source
AI summary
A method and a for location-based image retrieval, the method comprising using at least one hardware processor for: receiving N-dimensional coordinates of a point of interest, wherein N is an integer equal to or larger than 2; and retrieving one or more digital images in which the point of interest is visible. In addition, a computer program product configured to execute this method.