Search Query Categorization via Image Annotation
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Solution Overview
Problem
Existing internet search engines struggle to effectively categorize queries based on user behavior and image content, leading to irrelevant or unorganized search results.
Innovation Solution
A method that involves obtaining images from search results associated with user behavior data, selecting images that meet certain interaction thresholds, and annotating these images based on their content analysis. These annotations are then used to categorize queries, which can improve the ranking and relevance of subsequent search results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If search engines return image results based on traditional scoring functions, then search results can be generated quickly, but the relevance and quality of results deteriorate due to lack of query categorization
Solution Approach 1:
The system performs preliminary categorization of queries by analyzing user behavior data and image content in advance. Query categories and image annotations are pre-computed and stored, so when a search query is submitted, the system can quickly retrieve pre-analyzed data rather than performing full analysis in real-time, thus improving relevance without excessive time loss
Solution Approach 2:
The system dynamically adjusts search results based on query categories. Different category types (e.g., single person, diverse, text queries) trigger different ranking strategies and image selection criteria, allowing the search engine to adapt its behavior to the specific nature of each query while maintaining efficient processing
2Measurement precision
If search engines analyze all image content in detail, then annotation accuracy improves, but processing complexity and time increase
Solution Approach 1:
The system applies different levels of analysis to different images based on their relevance and user interaction patterns. High-value images that frequently appear in search results receive more detailed annotation, while less important images receive lighter processing. This localized quality approach maintains annotation accuracy for critical images while reducing overall system complexity
Solution Approach 2:
The system changes analysis parameters based on query category. For example, text queries trigger text detection parameters, while diverse queries trigger diversity assessment parameters. This dynamic parameter adjustment allows accurate annotation tailored to specific query types without requiring all possible analysis capabilities to be active simultaneously, reducing system complexity
3Productivity
If search engines use simple ranking criteria, then processing speed is maintained, but result diversity and quality deteriorate
Solution Approach 1:
The system incorporates user behavior feedback (clicks, selections, interactions) into the ranking process. Images that receive more user engagement are weighted more heavily in subsequent searches of the same category. This feedback mechanism allows the system to quickly adapt to user preferences and improve result diversity and quality while maintaining processing speed through efficient feedback loops
Solution Approach 2:
The system uses a unified query categorization framework that handles multiple query types (single person, diverse, text, etc.) through a single multi-functional categorization engine. This universal approach allows the system to maintain simple, fast processing logic while still achieving diverse and adaptable results across different query categories through the flexibility of the categorization system
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for query categorization based on image results. In one aspect, a method includes receiving images from image results responsive to a query, wherein each of the images is associated with an order in the image results and respective user behavior data for the image as a search result for the first query, and associating one or more of the first images with a plurality of annotations based on analysis of the selected first images' content.