Multiresolution Image Search via Hierarchical Feature Segmentation
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Solution Overview
Problem
Current image search technologies are limited in their ability to perform scalable and granular searches over large datasets, failing to capture hierarchical image features and lacking a modular approach, which restricts their functionality and usability, especially in handling large volumes of image data from sources like satellites.
Innovation Solution
A system and method for image data mining and searching that includes image capture devices, an indexing engine for categorization, an image database, and a search engine, allowing for multi-resolution and multi-temporal searches by decomposing images into pixel, region, and scene-level features, and enabling modular operation to accommodate incremental development without disrupting ongoing processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If basic image comparison operations are used, then simplicity of operation is maintained, but search precision and granularity are limited
Solution Approach 1:
The system segments image analysis into multiple hierarchical levels: pixel-level features (color, texture), region-level features (shapes, objects), and scene-level features (land cover, urban structures). This segmentation enables precise searches at different granularities while maintaining manageable system complexity through modular processing stages.
Solution Approach 2:
The system adds temporal and resolution dimensions to traditional image search. Multi-temporal analysis compares images across different time points, while multi-resolution processing analyzes images at various scales. These additional dimensions enable comprehensive search precision without overwhelming system complexity through structured dimensional expansion.
2Extent of automation
If human tagging is used for image search, then search capability is provided, but scalability and automation are limited
Solution Approach 1:
The system performs self-service by automatically extracting hierarchical features from images without human intervention. Automated algorithms identify pixel-level characteristics, detect region-level objects and shapes, and classify scene-level land cover and urban structures. This self-service approach achieves full automation while preserving rich hierarchical information that would be difficult for humans to systematically tag.
3Adaptability or versatility
If simple image comparison is used, then ease of operation is maintained, but adaptability and search scope are restricted
Solution Approach 1:
The system achieves universality by implementing a multi-functional search engine that handles diverse search types: spatial queries (find objects in specific locations), temporal queries (track changes over time), hierarchical queries (search at pixel, region, or scene level), and pattern recognition (identify specific land cover types or urban structures). This universal capability expands search scope while maintaining ease of operation through a unified interface.
4Productivity
If large volumes of image data are processed, then comprehensive search coverage is achieved, but processing time and system resources increase
Solution Approach 1:
The system performs preliminary action by pre-processing and indexing images upon ingestion. Hierarchical features (pixel-level, region-level, scene-level) are extracted and stored in optimized data structures during initial processing. Temporal and resolution variations are pre-computed and indexed. This preliminary action enables rapid query execution without re-processing entire datasets, significantly improving productivity while reducing search time.
Data Source
AI summary
A system for modular image mining and search, comprising a plurality of image capture devices, a search engine, an indexing engine, a database, and user interface software. A plurality of image capture devices capture images and store them to a database. The indexing engine indexes images to create image data for each image with associated metadata and stores the information to the database. The interface software is used to request search queries via a search engine. The search engine accesses the image data to find images or parts of images that satisfy the search query.