Composite Object Clustering for Faster Image Labeling
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Solution Overview
Problem
Current systems for object recognition in images are not always accurate, slow, and require excessive manual effort or computer resources, lacking the ability to automatically identify and label objects with high efficiency.
Innovation Solution
A system comprising an extraction module, a clustering module, and an indexing module that automatically identifies and labels objects in images by generating masks, grouping similar objects, and creating an index for future object identification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual labeling is used to train computer vision models, then object recognition accuracy can be improved, but the time consumption and manual effort increase significantly
Solution Approach 1:
The system automatically labels objects in images using computer vision algorithms and machine learning models, enabling the system to perform the labeling task itself without human intervention. This self-service approach eliminates manual effort while maintaining high accuracy through iterative training and validation processes
Solution Approach 2:
The system performs preliminary object detection and labeling on a subset of images to create training data, which is then used to train the model before deploying it for automated labeling of larger datasets. This preliminary action reduces the overall time required by establishing a foundation model that can be quickly applied
2Productivity
If traditional object detection methods are used, then implementation simplicity is maintained, but processing speed and efficiency are insufficient
Solution Approach 1:
The system segments the object recognition process into distinct modules including image preprocessing, feature extraction, object detection, labeling, and validation. This segmentation allows each module to be optimized independently for speed while maintaining overall system manageability through clear interfaces between components
Solution Approach 2:
The system introduces intermediate processing layers including feature extraction modules and prediction models that act as mediators between raw image data and final labels. These intermediaries enable efficient processing by transforming data into progressively more meaningful representations, improving speed without excessive complexity
3Extent of automation
If existing object recognition systems are used, then basic functionality is provided, but they require excessive computer resources and lack automation
Solution Approach 1:
The system dynamically adjusts processing parameters including model complexity, image resolution, and processing batch size based on available computational resources and performance requirements. This dynamic adaptation enables full automation while optimizing resource consumption by matching system capacity to task demands in real-time
Solution Approach 2:
The system changes key parameters such as model architecture, processing precision, and resource allocation based on the specific application requirements and available resources. This allows the same automated system to operate efficiently across different hardware configurations, reducing overall resource consumption while maintaining automation
Data Source
AI summary
A method for automatically clustering similar objects in images includes accessing a plurality of video frames of a video. The method further includes identifying a plurality of objects from the plurality of video frames. The method further includes generating a plurality of composite vectors for the plurality of objects by: generating a plurality of vectors for each particular object of the plurality of objects extracted from the plurality of video frames; and generating a particular composite vector for each particular object by combining the plurality of vectors for the particular object. The method further includes determining, using the composite vectors for the plurality of objects, a plurality of similar objects. The method further includes displaying images of one or more of the plurality of similar objects in a graphical user interface.