Foreground-Background Mask Training for Image Classification
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing machine learning models struggle to properly separate foregrounds and backgrounds in images during training, leading to poor performance in classification tasks.
Innovation Solution
A machine learning method that involves distinguishing foregrounds and backgrounds to generate mask images, cropping and augmenting these images, and adjusting the model parameters based on foreground rates and image sizes to improve training accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If images are used for model training without separating foregrounds and backgrounds, then the training process is simple, but the classification results are poor
Solution Approach 1:
The patent applies segmentation by dividing the image into foreground and background regions using mask images. The mask image distinguishes foreground objects from background areas, allowing the model to process and classify them separately. This segmentation improves classification reliability by ensuring that foreground and background features are not mixed during training.
Solution Approach 2:
The patent extracts the foreground region from the original image by using a mask image to identify and isolate the foreground object. This extraction process creates a focused training sample where only the relevant foreground features are used for classification, improving the model's ability to distinguish target objects from background distractions.
2Measurement precision
If mask images are generated to separate foregrounds and backgrounds, then classification accuracy improves, but the training process becomes more complex
Solution Approach 1:
The patent applies preliminary action by generating mask images before the main training process. The mask images are created in advance to pre-separate foreground and background regions, so that when training data is prepared, the separation is already done. This preliminary masking step improves distinguishing accuracy while managing complexity by performing the separation task before training begins.
3Reliability
If foregrounds and backgrounds are treated separately during training, then model performance improves, but the amount of processing required increases
Solution Approach 1:
The patent merges the mask image processing with the image training process by integrating them into a unified training pipeline. Instead of treating mask generation and image processing as separate operations, the system combines them so that both foreground and background features are extracted and processed together in a coordinated manner, improving model performance while maintaining training efficiency.
Data Source
AI summary
A method used in application of training a model of machine learning is described. Foregrounds and backgrounds of a first image are distinguished to generate a first mask image. The first image is cropped to generate second and third images. The first mask image is cropped to generate second and third mask images. Positions of the second and the third mask image correspond to positions of the second and the third image, respectively. First and second feature vector groups of the second image and the third image are generated by a model. A first matrix is generated according to the first and second feature vector groups. A second matrix is generated according to the second and third mask images. A function is generated according to the first and second matrices. The model is adjusted according to the function.