Image Label Identification Using Visual and Word Feature Fusion
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Solution Overview
Problem
Existing picture label identification algorithms lack semantic consideration, resulting in insufficient label diversity and low accuracy.
Innovation Solution
A label identification method that incorporates both visual and word features, leveraging semantic relationships between labels to enhance the identification process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a picture label identification algorithm directly identifies labels from zero using mapping relationship learning, then the label identification task can be completed, but the label diversity is insufficient and identification accuracy is not high enough
Solution Approach 1:
The patent segments the label identification process into two distinct stages: (1) visual feature extraction from the image, and (2) word feature extraction from label candidates. By separating these features and processing them independently before fusion, the system can handle diverse label types more effectively while maintaining high identification accuracy through targeted feature matching.
Solution Approach 2:
The patent merges visual features and word features into a unified target feature representation. This combination allows the system to leverage both image content information and label semantic information, thereby improving identification accuracy while supporting diverse label types through the complementary nature of the two feature sources.
2Adaptability or versatility
If the label identification is performed without considering semantic relationships between labels, then the computational process is simpler, but the label diversity and identification accuracy are insufficient
Solution Approach 1:
The patent performs preliminary extraction of word features from all candidate labels before the final identification decision. This advance preparation of label semantic features allows the system to efficiently evaluate multiple diverse label candidates without significantly increasing overall computational complexity, as the word feature extraction is done in advance and can be cached or reused.
3Reliability
If only visual features are used for label identification, then the process is simpler, but the semantic meaning and relationship between labels cannot be captured
Solution Approach 1:
The patent introduces word features as an intermediary representation that bridges visual image content and label semantic meaning. The word feature extraction process converts label text into numerical representations that can be compared with visual features, enabling the system to capture semantic relationships between labels while maintaining a manageable processing framework through the use of standardized feature vectors.
Data Source
AI summary
Provided are a label identification method and apparatus, a device, and a medium. The method includes: obtaining a target feature of a first image, in which the target feature characterizes a visual feature of the first image and a word feature of at least one label; and identifying a label of the first image from the at least one label based on the target feature. By characterizing the visual feature of the first image and the target feature of the word feature of the at least one label, the label of the first image is identified from the at least one label. Thus, identification accuracy of the label can be improved.