Dynamic Visual Search Engine Index Segmentation
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Solution Overview
Problem
Current visual search engines face challenges in efficiently searching large numbers of images while allowing for dynamic updates and additions, as they often require downtime or significant computational resources for indexing and querying.
Innovation Solution
A dynamic visual search engine system that partitions compressed image descriptors into database shards and maintains a separate index for uncompressed descriptors, allowing for seamless merging and swapping of database shards without disrupting query operations, enabling high-speed searching and immediate addition of new images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If a visual search engine uses a static index with compressed image descriptors partitioned into database shards, then searching efficiency is improved, but the system requires downtime for index updates and cannot accept new images dynamically
Solution Approach 1:
The system divides the index into two separate segments: a static compressed index partitioned into database shards for efficient searching, and a dynamic uncompressed index that can accept new images continuously. This segmentation allows each part to serve its specific function without interfering with the other, resolving the contradiction between searching efficiency and dynamic adaptability
Solution Approach 2:
The system implements a dynamic index that continuously accepts new image descriptors without requiring system downtime. This dynamic component operates independently from the static compressed index, enabling the system to adapt to new images in real-time while maintaining efficient search capabilities through the static index
2Adaptability or versatility
If a visual search engine maintains a dynamic index with uncompressed image descriptors for immediate image addition, then adaptability is improved, but computational resources and memory usage increase
Solution Approach 1:
The system applies different quality characteristics to different parts of the index: the dynamic index uses uncompressed descriptors for immediate adaptability, while the static index uses compressed descriptors for space efficiency. This local differentiation allows the system to optimize memory usage by only keeping uncompressed descriptors for recently added images that require dynamic access
3Quantity of substance
If a visual search engine merges uncompressed image descriptors into compressed database shards, then storage efficiency is improved, but query operations may be disrupted during merging
Solution Approach 1:
The system performs preliminary actions by maintaining the dynamic uncompressed index as a buffer before merging into the static compressed index. This preliminary staging area allows merging operations to be prepared and executed without disrupting ongoing query operations, as the dynamic index continues to serve new image insertions during the merge process
Data Source
AI summary
Systems and methods for a dynamic visual search engine are provided. In one example method, a criteria used to partition a set of compressed image descriptors into multiple database shards may be determined. Additionally, a size of a dynamic index may be determined. The dynamic index may represent a dynamic number of images and may be configured to accept insertion of reference images into the dynamic index that can be search against immediately. According to the method, an instruction to merge the uncompressed image descriptors of the dynamic index into the database shards of the compressed image descriptors may be received, and the uncompressed image descriptors of the dynamic index may be responsively merged into the database shards of the compressed image descriptors based on the criteria.