Contextual Image Search via Topic Match Scoring
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Solution Overview
Problem
Image search systems often present irrelevant images in search results due to mismatched content between images and their hosting resources, leading to user dissatisfaction and increased query revisions.
Innovation Solution
A data processing apparatus determines resource topics from text and image topics from images, calculating topic match scores to rank images based on topical similarity, incorporating image characteristics and prominence scores to refine relevance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If image search systems rank images based on traffic and tag relevance alone, then high-traffic images are presented quickly, but the images may be topically irrelevant to the user's actual information needs
Solution Approach 1:
The system performs preliminary topic extraction from resource text and image content before the search query is submitted. Topic match scores are pre-calculated and stored in an index, so when a query arrives, the system can quickly retrieve and rank images based on pre-computed topical relevance rather than analyzing everything from scratch during query processing.
Solution Approach 2:
The patent introduces 'topic match score' as an intermediary metric that bridges the gap between image content and resource context. This intermediary score combines tag relevance with textual topic analysis, serving as a mediator that filters and ranks images based on their actual topical connection to the resource, rather than relying solely on traffic or simple tag matching.
2Measurement precision
If the system analyzes both resource text and image content to determine topic match scores, then image relevance accuracy improves, but processing complexity increases
Solution Approach 1:
The system segments the topic analysis process into distinct components: resource text topic extraction, image content topic extraction, and topic match scoring. Each component operates independently and can be processed in parallel. The segmentation allows the system to handle complex multi-modal analysis by breaking it into manageable, specialized modules that can be optimized separately.
Solution Approach 2:
Topic extraction from both resource text and image content is performed in advance during indexing, not during query processing. This preliminary action pre-computes the topic representations and stores them for rapid retrieval, shifting the computational burden from query time to indexing time, thereby reducing the complexity experienced during actual search operations.
3Productivity
If the system pre-processes and indexes topic information for all images and resources, then query processing speed improves, but indexing complexity and resource requirements increase
Solution Approach 1:
The system employs automated topic extraction algorithms that self-serve the indexing process without requiring manual curation or intervention. The topic extraction and match scoring are performed automatically during the indexing phase, allowing the system to handle large volumes of images and resources autonomously, which justifies the indexing complexity through automation rather than manual processes.
Solution Approach 2:
Comprehensive topic analysis and match scoring are performed in advance during the indexing phase, creating a rich pre-processed data structure. This preliminary action stores extracted topics, tags, and pre-computed match scores in the index, enabling extremely fast query processing by simply retrieving and ranking based on pre-computed values rather than performing heavy analysis during query execution.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium determining image relevance responsive to a search query. A method includes, for each resource in a plurality of resources, wherein each resource includes one or more images and text that is separate from each of the one or more images: determining, by a data processing apparatus, and from the text of the resource, resource topics described by the text of the resource. For each of the one or more images, processing the image to determine a set of image topics that describe topics to which content depicted in the image belongs. Determining, by the data process apparatus, one or more topic match scores, wherein each topic match score is a measure of relevance one or more of the image topics of the image to one or more of the resource topics of the resource.