Image Sorting Device Group Feature Classification
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Solution Overview
Problem
Existing image classification technologies struggle to accurately group images from the same event together, as they often rely on image features or capture time, leading to misclassification of images within the same group into different categories.
Innovation Solution
An image classification device that calculates image group feature information based on image features for all or a portion of images within a group, allowing for classification into predefined categories without separating images from the same event.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If images are classified based on individual image features, then classification can be performed, but images from the same event group may be separated into different categories
Solution Approach 1:
The patent merges multiple image features (color, texture, shape, capture time) into a unified image group feature representation. By combining these diverse features and classifying based on the aggregated group characteristics rather than individual images, the system maintains both classification precision and group consistency, preventing separation of related images into different categories.
2Reliability
If images are classified based on capture time, then images from the same event can be grouped, but images cannot be classified based on other image features
Solution Approach 1:
The patent creates a universal classification system that handles multiple types of features (color, texture, shape, capture time) through a single image group feature calculation mechanism. This multi-functional approach allows the system to adapt to different classification needs while maintaining group consistency, enabling versatile classification based on various feature combinations without requiring separate classification systems for each feature type.
3Adaptability or versatility
If multiple image features are extracted and used for classification, then more detailed classification is possible, but the complexity of the classification system increases
Solution Approach 1:
The patent segments the classification process into distinct modular components: color feature extraction, texture feature extraction, shape feature extraction, and capture time feature extraction. Each module independently processes one type of feature and outputs standardized feature data. This segmentation allows the system to handle multiple feature types with high adaptability while keeping each component simple and manageable, thereby reducing overall system complexity.
Data Source
AI summary
Image group feature information indicating features of an image group composed of a plurality of images is calculated for each image group and, based on the calculated image group feature information and information indicating features of events into which image groups are to be classified, images are classified by image group.