Image Description Templates Using Seed Descriptors and Document Structure
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Solution Overview
Problem
Current search systems face challenges in generating descriptive text for images, as they rely on text associated with images, which may not be consistently available or relevant, leading to suboptimal image search results.
Innovation Solution
The method involves identifying seed descriptors for images, generating structure information, and creating templates that include image location, document structure, and image feature information to generate descriptive text for other images, improving image search relevance by associating descriptive text with images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If search systems rely on text associated with images (such as labels or metadata), then image identification can be performed, but the descriptive text may not accurately represent the image content, leading to suboptimal search results
Solution Approach 1:
The patent introduces an intermediary process that extracts text from the surrounding document context (captions, paragraphs, titles) rather than relying directly on image metadata or labels. This intermediary text extraction and template application process serves as a mediator between the image and its descriptive representation, improving accuracy by using contextually relevant text from the document where the image appears.
2Measurement precision
If templates are generated for each seed descriptor to capture document structure, then descriptive text generation accuracy improves, but system complexity increases
Solution Approach 1:
The patent segments the document structure into identifiable components (image location, caption position, paragraph structure, title hierarchy) and creates specific template patterns for each segment type. This segmentation allows the system to handle complex document structures through modular, reusable template patterns rather than requiring a single complex processing mechanism.
Solution Approach 2:
The patent creates template copies that can be applied to multiple images with similar structural contexts. Once a template is generated from one seed descriptor and document structure, it can be copied and applied to other images following the same structural pattern, reducing the need to create entirely new processing logic for each case.
3Reliability
If the system generates structure information and templates for multiple seed descriptors, then image search relevance improves, but processing time and computational resources increase
Solution Approach 1:
The patent performs preliminary actions by pre-generating templates from seed descriptors and storing them for later use. During actual image search operations, the system applies these pre-generated templates rather than creating them from scratch, significantly reducing processing time while maintaining search relevance.
Solution Approach 2:
The patent changes parameters by adjusting template matching thresholds and selection criteria based on the specific search context and image type. This allows the system to optimize between processing speed and search relevance by modifying parameters such as template confidence thresholds, number of templates applied, and text extraction depth without fundamentally changing the system architecture.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating descriptive text for images. In one aspect, a method includes identifying a set of seed descriptors for an image in a document that is hosted on a website. For each seed descriptor, structure information is generated that specifies a structure of the document with respect to the image and the seed descriptor. One or more templates are generated for each seed descriptor using the structure information for the seed descriptor. Each template can include image location information, document structure information, image feature information, and a generative rule that generates descriptive text for other images in other documents. Descriptive text for other images is generated using the templates and the other documents. The descriptive text is associated with the images.