Hierarchical Image Positioning for Indoor Localization Clustering
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing image positioning technologies face challenges in accurately clustering large amounts of images for indoor localization, particularly in environments with uniform decoration styles, leading to inefficient and inaccurate positioning results.
Innovation Solution
An image positioning device and method utilizing a deep learning algorithm and clustering algorithms to classify images into main and sub-groups based on feature vectors and relative moving distances, enabling precise positioning of sub-groups within their corresponding areas.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If large amounts of images are clustered to build a reference database for indoor localization, then positioning coverage is improved, but computational cost and query time increase significantly
Solution Approach 1:
The patent segments the large image database into multiple clusters based on spatial relationships and feature similarities. Each cluster represents a localized group of images, allowing the system to search only relevant clusters during positioning queries rather than the entire database, thus reducing query time while maintaining comprehensive coverage
Solution Approach 2:
The patent performs preliminary clustering and organization of images into structured groups before actual positioning queries. By pre-processing and arranging images into clusters with defined spatial relationships, the system prepares the data structure in advance to enable faster retrieval during positioning operations
2Measurement precision
If more images are clustered in the reference database, then positioning accuracy is improved, but device complexity and computational cost increase
Solution Approach 1:
The patent applies different processing and clustering strategies to different regions or types of images based on their local characteristics. By adapting the clustering approach to local image properties rather than applying a uniform method, the system achieves high positioning accuracy while avoiding the complexity of processing every image with the most computationally intensive methods
Solution Approach 2:
The patent implements a multi-level clustering approach where images are first grouped into coarse clusters, then refined into sub-clusters only where necessary for achieving positioning accuracy. This partial application of detailed clustering avoids the excessive computational complexity of fully detailed clustering across all images while maintaining sufficient accuracy
3Ease of manufacture
If traditional clustering algorithms are used for image classification, then implementation simplicity is maintained, but positioning accuracy deteriorates in environments with uniform decoration styles
Solution Approach 1:
The patent extends traditional clustering by incorporating multiple dimensions of image analysis including spatial coordinates, feature vectors, and hierarchical relationships. By adding these additional dimensions to the clustering process, the system can distinguish between images in uniform environments that traditional single-dimension algorithms would confuse, thereby improving accuracy while maintaining reasonable implementation complexity
Data Source
Figure 1
Figure 2
Figure 3
AI summary
An image positioning device is provided in the invention. The image positioning device includes a calculation circuit and a positioning circuit. The calculation circuit obtains a plurality of images from an image capturing device and classifies the images into a plurality of main groups based on a first algorithm, wherein each main group corresponds to a different area and the calculation circuit classifies the images of each main group into a plurality of sub-groups based on a feature of each image of each main group and a clustering algorithm. The positioning circuit is coupled to the calculation circuit. The positioning circuit positions each sub-group in the area corresponding to each main group based on the relative position relationship between each sub-group of each main group.