Visual Search Recommendation Engine Using Image Quality Indication
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Solution Overview
Problem
Conventional search engines fail to provide accurate image recommendations for visual search queries, as they do not effectively consider both image quality indication and image similarity, leading to broad and inaccurate search results that misrepresent user intent, making it difficult for users to find relevant information.
Innovation Solution
A visual search recommendation system that uses an image quality indication model and an image similarity determination model to identify and recommend images that exceed the search query performance, based on factors like angle of view, blurriness, background quality, and similarity to the search image, ensuring more accurate and relevant search results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional search engines use only basic query matching for visual search, then the search process is simple and fast, but the search results are broad and inaccurate, failing to represent user intent
Solution Approach 1:
The system performs preliminary evaluation of image quality indication and image similarity before generating final search results. By pre-assessing these dimensions on the search image, the system can filter and rank results more accurately without completely redesigning the search pipeline, thus improving accuracy while controlling complexity
Solution Approach 2:
The patent introduces two new evaluation dimensions (image quality indication and image similarity) beyond traditional text-based query matching. This multi-dimensional approach allows the system to assess visual search queries from multiple angles, improving result accuracy by considering both the quality of the search image and its similarity to corpus images
2Measurement precision
If the system evaluates multiple image dimensions (quality and similarity) for each search query, then search result accuracy improves, but processing time and computational resources increase
Solution Approach 1:
The system computes image quality indication and image similarity metrics in advance as preliminary steps before final result generation. By performing these evaluations upfront on the search image itself, the system avoids redundant computations during result retrieval, thus improving relevance while managing processing time
Solution Approach 2:
The search image itself provides the evaluation criteria through its inherent properties. The image quality indication model and image similarity determination model use the search image's own characteristics to evaluate potential matches, eliminating the need for external reference standards and reducing computational overhead
3Adaptability or versatility
If the system provides detailed image recommendations with quality and similarity metrics, then user intent is better understood, but the number of required user inputs increases
Solution Approach 1:
The system automatically evaluates and understands user intent by analyzing the uploaded search image itself. The image quality indication and image similarity models extract meaningful information directly from the user's input image without requiring users to provide additional metadata, descriptions, or multiple inputs, thus improving intent understanding while maintaining ease of operation
Data Source
AI summary
A visual search recommendation engine utilizes an image quality indication model for a visual search recommendation. Specifically, the visual search recommendation engine receives a search image as a search query at a search engine. The visual search recommendation engine provides the search image as an input into the image quality indication model, which is trained to output an image quality indication based on image aspects of the search image. A plurality of images are identified from an image corpus. The visual search recommendation engine determines an image similarity based on a comparison between the plurality of images from the image corpus and the search image. The image quality indication and the image similarity indicate a search query performance for the search image. A first image exceeding the search query performance is identified. The first image is provided for display at the search engine.