Image Matching Service Keyword Refinement for Search Precision
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Solution Overview
Problem
Conventional image search technologies face challenges in accurately identifying specific products from images, especially when image quality is poor or when products share similar branding, leading to irrelevant search results and user confusion.
Innovation Solution
An image matching service that provides recommended search keywords associated with image data, allowing users to refine their searches and filter out irrelevant products by selecting specific keywords related to the intended product, thereby improving search precision and relevance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If image-based search is used to identify products, then search speed is improved, but search precision deteriorates due to multiple irrelevant matches
Solution Approach 1:
The search process is segmented into two stages: first, image-based matching provides rapid initial results; second, keyword-based filtering refines these results. This segmentation allows the system to benefit from both the speed of image matching and the precision of keyword searching, resolving the contradiction between search speed and precision.
Solution Approach 2:
Keywords serve as an intermediary element between the image query and the final product identification. The system extracts keywords from matched images and uses them as a mediator to filter and refine results, enabling users to achieve precise product identification while maintaining fast search performance.
2Ease of operation
If image quality is low, then ease of use is improved (users can search with any photo), but measurement precision deteriorates (difficulty to identify the product)
Solution Approach 1:
The system performs preliminary keyword extraction from multiple matched images before presenting final results. By pre-processing and aggregating keywords from various image matches, the system compensates for low image quality and ensures accurate product identification even when input images are poor quality.
Solution Approach 2:
The system provides feedback to users in the form of extracted keywords and multiple matched results. This feedback loop allows users to see what the system has identified and adjust their search if needed, improving accuracy even with low-quality input images while maintaining ease of use.
3Adaptability or versatility
If multiple product types are matched to a single image, then adaptability is improved (search works for various products), but device complexity increases (difficulty to identify the intended product)
Solution Approach 1:
The system extracts keywords from the matched images and separates them as distinct filtering options. This extraction allows users to take out specific product attributes or types from the mixed results and use them to filter, reducing the complexity of navigating through diverse product types while maintaining the adaptability of the search system.
Data Source
AI summary
Techniques for providing recommended keywords in response to an image-based query are disclosed herein. In particular, various embodiments utilize an image matching service to identify recommended search keywords associated with image data received from a user. The search keywords can be used to perform a keyword search to identify content associated with an image input that may be relevant. For example, an image search query can be received from a user. The image search query may result in multiple different types of content that are associated with the image. The system may present keywords associated with matching images to allow a user to further refine their search and/or find other related products that may not match with the particular image. This enables users to quickly refine a search using keywords that may be difficult to identify otherwise and to find the most relevant content for the user.