Image Object Search via Detected Metadata and Feedback Loops
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Solution Overview
Problem
As the collection of digital images grows, managing them becomes increasingly difficult due to the generated data from automatic detection processes, which often include imperfections such as undetected objects and false alarms, necessitating a more efficient method to search and organize images based on detected objects.
Innovation Solution
A system comprising an object detector, identifier, and manager that processes images to detect objects, associate them with metadata, and allow users to search through collections using various criteria, including object characteristics, tags, and search interfaces, enabling efficient retrieval of images matching specified search parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Difficulty of detecting and measuring
If automatic detection processes are used to detect objects in images, then object detection capability is improved, but false alarms and undetected objects increase
Solution Approach 1:
The system uses detected objects and their metadata to generate search criteria, allowing users to refine and verify detection results through iterative searching. The feedback loop enables users to adjust search parameters based on initial detection outcomes, improving overall detection reliability.
Solution Approach 2:
Metadata acts as an intermediary between the automatic detection process and the final search results. By associating detected objects with additional information and using this metadata to generate search criteria, the system bridges the gap between automated detection and accurate object retrieval.
2Quantity of substance
If the collection of images grows, then the amount of data increases, but managing and searching through the collection becomes more difficult
Solution Approach 1:
The system segments the large image collection by detecting and categorizing objects within each image. By organizing images based on detected objects and their associated metadata, the system divides the overwhelming task of managing entire images into manageable object-level categories, enabling more efficient search and organization.
Solution Approach 2:
Metadata serves as an intermediary layer between the raw image data and the search interface. This metadata layer provides structured information about detected objects, allowing users to search and manage large collections without directly interacting with the full complexity of the image data.
3Productivity
If automatic detection processes generate data from images, then object information is extracted, but the generated data contains imperfections requiring additional processing
Solution Approach 1:
The system performs preliminary object detection and metadata generation automatically, then uses this preliminary data to construct search criteria. This preliminary action allows the system to process large numbers of images efficiently while providing a foundation for subsequent refinement and verification steps.
Solution Approach 2:
The system implements feedback loops where detection results inform search criteria, which in turn refine the detection outcomes. Users can iteratively adjust search parameters based on initial results, allowing the system to correct imperfections and improve data quality through multiple passes.
Data Source
AI summary
Presenting a subset is disclosed. Information associated with a set of one or more objects is obtained, where the set of one or more objects have been detected from a collection of one or more images. Object search criteria is obtained. A subset of the collection is determined based at least in part on the object search criteria. The subset is presented.