Multiple Clustered Instance Learning for Image Classification
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Solution Overview
Problem
Conventional image classification models require labor-intensive and time-consuming strongly supervised learning with detailed manual annotations, making them inefficient for large-scale image classification tasks involving multiple visual concepts.
Innovation Solution
The development of a multiple clustered instance learning (MCIL) model that performs image-level classification, patch-level clustering, and pixel-level segmentation in a unified framework, using weakly supervised machine learning to reduce human involvement by automatically learning classifiers from training images with high-level labels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If strongly supervised learning with detailed manual annotations is used, then labeling accuracy is improved, but labor intensity and time consumption increase significantly
Solution Approach 1:
The image is segmented into multiple patches or instances, allowing the model to process and classify different regions independently. This segmentation enables weakly supervised learning to achieve accurate localization by learning instance-level classifiers for each patch, resolving the contradiction between accuracy and time consumption by distributing the annotation burden across multiple smaller units rather than requiring full image annotation
Solution Approach 2:
The patent introduces instance-level classifiers as an intermediary between the weakly supervised image-level labels and the final segmentation results. These classifiers act as mediators that automatically learn from minimal annotations and propagate the labeling information to individual instances, achieving accurate instance-level classification without requiring direct manual annotation of each instance
2Measurement precision
If detailed manual annotations of multiple visual concepts are required, then classification accuracy is improved, but scalability to large numbers of images deteriorates
Solution Approach 1:
The MCIL model achieves multi-functionality by simultaneously performing image-level classification, instance-level classification, and localization tasks within a single unified framework. This universal approach allows the system to handle multiple visual concepts and classification objectives without requiring separate annotation processes for each task, thereby maintaining high classification accuracy while improving scalability to large datasets
Solution Approach 2:
The system employs self-service through automatic instance-level classifier learning that requires minimal human intervention. The model automatically learns from weakly supervised image-level labels and generates instance-level classifications and segmentations without requiring manual annotation of each visual concept, enabling the system to scale to large numbers of images while maintaining accurate multi-concept classification
3Loss of information
If conventional strongly supervised learning is used, then detailed visual concept labeling is achieved, but human involvement and labor intensity increase
Solution Approach 1:
The patent implements dynamics by transitioning from static manual annotation processes to dynamic automatic learning processes. The instance-level classifiers are dynamically trained and refined during the learning process, automatically adapting to extract visual concept information from images without requiring fixed manual annotation protocols, thereby reducing human involvement while preserving comprehensive visual concept information
Solution Approach 2:
The patent replaces the mechanical system of manual annotation with an automated machine learning system. Instead of relying on human experts to manually label visual concepts, the system uses instance-level classifiers that automatically learn and extract visual concept information from images, substituting human labor with computational processes while maintaining or improving the quality of visual concept information extraction
Data Source
AI summary
The techniques and systems described herein create and train a multiple clustered instance learning (MCIL) model based on image features and patterns extracted from training images. The techniques and systems separate each of the training images into a plurality of instances (or patches), and then learn multiple instance-level classifiers based on the extracted image features. The instance-level classifiers are then integrated into the MCIL model so that the MCIL model, when applied to unclassified images, can perform image-level classification, patch-level clustering, and pixel-level segmentation.