Content Search Matching with Minimum Region Tests
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Solution Overview
Problem
Current search technologies for diverse content types, such as images, face inefficiencies and high false positive rates due to variations in query content quality and perspective, leading to unsatisfactory matching results, especially with feature-sparse content like icons and logos.
Innovation Solution
The implementation of a system that generates and correlates content descriptors for both query and content collections, using techniques like minimum content region and feature per scale tests, and adding blurred versions of feature-sparse content to enhance matching capabilities, allowing for robust and inexact matching.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If inexact matching is used to allow low quality query content, then search flexibility improves, but false positive match rate increases
Solution Approach 1:
The patent introduces multiple dimensions for content comparison beyond simple pixel matching, including color histograms, spatial relationships, and semantic features. This multi-dimensional approach allows the system to tolerate variations in query quality while maintaining matching accuracy by evaluating content similarity across multiple independent feature spaces.
Solution Approach 2:
The system dynamically adjusts matching parameters and thresholds based on query content quality assessment. When low quality query content is detected, the system modifies matching sensitivity, adjusts tolerance levels for feature variations, and adapts weighting schemes to maintain reliable results despite reduced input quality.
2Reliability
If exact matching is used to reduce false positives, then matching reliability improves, but search flexibility decreases
Solution Approach 1:
The patent implements dynamic matching criteria that adapt based on query characteristics, content type, and user preferences. The system transitions between exact and inexact matching modes dynamically, adjusting the strictness of matching rules according to the specific search context rather than applying a fixed matching threshold.
Solution Approach 2:
The matching process is divided into multiple independent stages, each evaluating different aspects of content similarity. This segmented approach allows the system to apply exact matching for critical features while using more flexible matching for less important attributes, achieving both reliability and flexibility simultaneously.
3Productivity
If feature-sparse content is searched using traditional methods, then simple queries are fast, but matching accuracy deteriorates
Solution Approach 1:
The system performs preliminary processing of feature-sparse content during indexing, pre-computing and storing multiple derived features and representations. This advance preparation enables faster and more accurate matching during query processing by having pre-extracted features readily available without requiring complex real-time computation.
Solution Approach 2:
The patent introduces intermediary feature representations that bridge the gap between simple feature-sparse content and complex matching requirements. These intermediary features serve as mediators that enrich the representation of sparse content while maintaining processing efficiency, enabling accurate matching without sacrificing speed.
Data Source
AI summary
Systems and approaches for searching a content collection corresponding to query content are provided. In particular, false positive match rates between the query content and the content collection may be reduced with a minimum content region test and/or a minimum features per scale test. For example, by correlating content descriptors of a content piece in the content collection with query descriptors of the query content, the content piece can be determined to match the query content when a particular region of the content piece and/or a particular region of a query descriptor have a proportionate size meeting or exceeding a specified minimum. Alternatively, or in addition, the false positive match rate between query content and a content piece can be reduced by comparing content descriptors and query descriptors of features at a plurality of scales. A content piece can be determined to match the query content according to descriptor proportion quotas for the plurality of scales.