Image Captioning With Similarity Scoring for Representative Tags
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Solution Overview
Problem
Existing automated methods for generating image-specific technologies have difficulty in generating informative and concise image labels have not effectively addressed the challenge of generating informative and effective image generation of existing technologies have not effectively addressed the challenge of addressing the challenge of addressing the challenge of generating specific and relevant tags for a set of images, specifically addressing the challenge of addressing the challenge of addressing the challenge of addressing the challenge of providing a concise and informative representation of the image content.
Innovation Solution
A method and apparatus for generating representative tags involves obtaining images and their associated tags, computing image-tag similarity scores in a multi-modal embedding space, scaling dimensions based on variance, and selecting a representative tag with the highest classification score by averaging similarity scores across the image set.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated methods are used to generate image tags, then productivity is improved by eliminating manual annotation, but the quality and relevance of generated tags deteriorate
Solution Approach 1:
The system computes image-tag similarity scores and uses them to generate classification scores that feed back into the tag selection process. This feedback mechanism allows the system to iteratively refine tag selections based on measured similarity, improving tag relevance while maintaining automated efficiency
Solution Approach 2:
The patent replaces manual mechanical tagging with an automated computational system that uses multi-modal embedding spaces and similarity scoring. This substitution maintains productivity while improving quality through consistent algorithmic application rather than variable human judgment
2Ease of operation
If generic tagging methods are used, then ease of operation is improved by simplifying the tagging process, but the specificity and informativeness of tags deteriorate
Solution Approach 1:
The system computes individual similarity scores for each image-tag pair, allowing each tag to be evaluated based on its specific relevance to each image rather than applying a uniform generic tagging approach. This local evaluation preserves information specificity while maintaining operational simplicity through automation
Solution Approach 2:
The tagging system dynamically adjusts tag selection based on computed similarity scores rather than applying static generic tags. This dynamic approach ensures tags are specific to each image's content while keeping the process simple through automated score-based selection
3Reliability
If multiple tags are generated for each image, then the completeness of image representation is improved, but the complexity of selecting representative tags deteriorates
Solution Approach 1:
The system introduces classification scores as an intermediary metric that simplifies the selection of representative tags from multiple generated tags. Instead of directly comparing multiple tags across images, the classification scores serve as a mediator that aggregates similarity information, reducing selection complexity while maintaining representation completeness
Data Source
AI summary
A method, apparatus, non-transitory computer readable medium, apparatus, and system for image processing include obtaining a plurality of images and a plurality of tags, wherein each of the plurality of tags represents a corresponding element of at least one of the plurality of images, computing a plurality of image-tag similarity scores, wherein each of the plurality of image-tag similarity scores indicate a similarity between one of the plurality of images and one of the plurality of tags, computing a plurality of classification scores corresponding to the plurality of tags, respectively, by averaging a subset of the plurality of image-tag similarity scores corresponding to each of the plurality of tags, and selecting a representative tag for the plurality of images based on the representative tag having a highest classification score among the plurality of classification scores.