Compact Visual Vocabulary for Image Recognition
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Solution Overview
Problem
Current image recognition technologies face challenges in optimizing the number of descriptors that can be represented by a compact dictionary, leading to inefficient client-server communication and decreased responsiveness due to large data sizes, particularly in bandwidth-sensitive wireless channels.
Innovation Solution
A global descriptor vocabulary system is developed, which generates a compact dictionary by clustering descriptor sets into tessellated cells, assigning each cell a representative descriptor and an index, allowing for efficient mapping of large descriptors to small indices, enabling fast and accurate image recognition across various domains.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If a compact dictionary with limited visual words is used, then memory space and data transmission size are reduced, but the number of representable descriptors and recognition accuracy deteriorate
Solution Approach 1:
The patent segments the descriptor space into multiple clusters, each represented by a visual word. Instead of using a single large dictionary, the system divides the descriptor space into K clusters (where K is much smaller than the total number of descriptors), allowing efficient representation while maintaining coverage of the entire descriptor space through hierarchical organization.
Solution Approach 2:
The patent introduces a hierarchical dimension to the dictionary structure by organizing visual words into a vocabulary tree with multiple levels. This transforms a flat, single-level dictionary into a multi-level hierarchical structure, enabling compact representation while preserving fine-grained descriptor distinctions through the tree's depth and branching factor.
2Measurement precision
If more descriptors are clustered into visual words, then recognition accuracy improves, but dictionary size and memory requirements increase
Solution Approach 1:
The patent segments both the descriptor space and the dictionary structure. By dividing descriptors into K clusters and organizing them in a hierarchical tree with L levels and B branches per node, the system achieves comprehensive descriptor coverage while keeping the dictionary size manageable through the relationship: dictionary_size ≈ B^(L-1) × B_log_B(K), which is much smaller than storing all descriptors individually.
Solution Approach 2:
The patent implements a nested hierarchical structure where visual words are organized into parent-child relationships across multiple tree levels. Each node in the vocabulary tree contains child nodes, creating a nested organization that allows progressive refinement from coarse to fine descriptor categories, enabling accurate representation with compact storage.
3Adaptability or versatility
If a larger vocabulary is used to represent more objects, then recognition scope increases, but client-server communication overhead and response time increase
Solution Approach 1:
The patent segments the large vocabulary into a hierarchical tree structure with K visual words distributed across L levels with B branches per node. This segmentation allows the system to handle millions of objects by organizing their descriptors into manageable clusters, reducing communication overhead when transmitting descriptor information between client and server while maintaining broad recognition scope.
Solution Approach 2:
The patent performs preliminary clustering and organization of descriptors into the hierarchical vocabulary structure before actual recognition tasks. This pre-processing creates a ready-to-use compact representation that speeds up subsequent client-server communication and recognition operations, as descriptors can be quickly mapped to visual words without requiring real-time computation of large descriptor sets.
Data Source
AI summary
Systems and methods of generating a compact visual vocabulary are provided. Descriptor sets related to digital representations of objects are obtained, clustered and partitioned into cells of a descriptor space, and a representative descriptor and index are associated with each cell. Generated visual vocabularies could be stored in client-side devices and used to obtain content information related to objects of interest that are captured.