Image Tag Vector Similarity for Fine-Grained Hairstyle Classification
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Solution Overview
Problem
Annotating tasks with numerous categories and ambiguous states between them are challenging due to the difficulty in accurately distinguishing between categories with small differences, such as in a 45-category hairstyle classification task.
Innovation Solution
The method generates image tag vectors based on multiple attributes and calculates similarity with category tag vectors to quickly match images with their most similar categories, improving annotating quality and efficiency by using independent attributes and sorting tags by similarity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional multi-person or multi-round annotating voting mechanisms are used, then annotating accuracy can be improved for tasks with fewer and clearer categories, but annotating efficiency deteriorates significantly when facing tasks with numerous categories and ambiguous states
Solution Approach 1:
The patent replaces the mechanical voting mechanism (human annotators manually voting across multiple rounds) with an automated computational system. The system uses image recognition models to generate initial annotations and employs vector space similarity calculations to automatically determine category assignments, eliminating the need for manual multi-round voting while maintaining or improving accuracy.
Solution Approach 2:
The patent introduces an intermediary computational layer between the image and the final category annotation. This intermediary system uses pre-trained recognition models to extract features, converts them to tag vectors, and calculates similarities with category vectors to determine annotations, serving as a mediator that bridges raw image data and structured category labels efficiently.
2Adaptability or versatility
If the number of categories increases to handle more granular classification, then classification capability improves, but the difficulty of accurately distinguishing between categories with small differences increases
Solution Approach 1:
The patent transforms the category distinction problem from a direct visual comparison task into a vector space similarity task. By representing both images and categories as vectors in a high-dimensional space, the system can distinguish between categories with small visual differences by measuring angular or Euclidean distances in the vector space, which captures semantic relationships beyond pixel-level differences.
Solution Approach 2:
The patent changes the parameter space for category distinction from raw pixel values to extracted feature vectors. By transforming images into tag vectors based on pre-trained recognition models, the system operates in a parameter space where categories with small visual differences are better separated, making distinction easier even with numerous fine-grained categories.
3Measurement precision
If comprehensive attributes are used to characterize categories, then annotating quality improves, but the complexity of the annotating process increases
Solution Approach 1:
The patent performs preliminary action by pre-training recognition models and pre-establishing the relationship between tags, attributes, and categories before the actual annotating task. This preprocessing creates a ready-to-use vector space and similarity metrics, so that during actual annotation, the system only needs to perform straightforward vector comparisons rather than complex attribute analysis, reducing process complexity while maintaining quality.
Data Source
AI summary
The present disclosure relates to an image annotating method, classification method and machine learning model training method, and to the field of computer technologies. The image annotating method includes: generating an image tag vector of image to be annotated, according to a plurality of attributes for image annotating and multiple tags corresponding to each of the attributes; annotating an image category to which the image to be annotated belongs, according to vector similarity between the image tag vector and an category tag vector of each of a plurality of image categories, the category tag vector being generated according to the multiple tags corresponding to each of the attributes.