International business machines corporation (20240096068). AUTO-GROUPING GALLERY WITH IMAGE SUBJECT CLASSIFICATION simplified abstract
Contents
AUTO-GROUPING GALLERY WITH IMAGE SUBJECT CLASSIFICATION
Organization Name
international business machines corporation
Inventor(s)
Yuan Yuan Gong of Shanghai (CN)
AUTO-GROUPING GALLERY WITH IMAGE SUBJECT CLASSIFICATION - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240096068 titled 'AUTO-GROUPING GALLERY WITH IMAGE SUBJECT CLASSIFICATION
Simplified Explanation
The abstract of the patent application describes a method where a computer processor can replace visual words of an unsupervised machine learning classification model with visual objects of an image. The model can be augmented to represent the image as a mixture of subjects, with each subject represented by placements of visual objects in a three-dimensional space. The processor learns latent relationships between the placements of visual objects and image semantics to classify image subjects.
- The patent describes a method where a computer processor replaces visual words with visual objects in an unsupervised machine learning classification model.
- Co-occurring single visual objects in an image can be combined to form compound visual objects.
- The model represents the image as a mixture of subjects, with each subject represented by placements of visual objects in a three-dimensional space.
- The processor learns latent relationships between visual object placements and image semantics to classify image subjects.
- Potential Applications
This technology can be applied in image recognition systems, content-based image retrieval, and automated image tagging.
- Problems Solved
This technology solves the problem of accurately classifying image subjects without the need for manual labeling or supervision.
- Benefits
The benefits of this technology include improved accuracy in image subject classification, automation of image analysis tasks, and scalability in handling large volumes of image data.
- Potential Commercial Applications
Potential commercial applications of this technology include image search engines, social media platforms for automated tagging, and surveillance systems for object recognition.
- Possible Prior Art
Prior art in this field may include research on unsupervised machine learning models for image classification and object recognition systems.
- Unanswered Questions
- How does this technology handle complex images with multiple subjects and objects?
This technology can handle complex images by learning the relationships between visual objects and image semantics to classify multiple subjects accurately.
- Can this technology be applied to real-time image processing applications?
Yes, this technology can be applied to real-time image processing applications by optimizing the learning process and model inference for faster classification.
Original Abstract Submitted
at least one computer processor can replace visual words of an unsupervised machine learning classification model with visual objects of an image. at least two co-occurring single visual objects adjacent to each other in pixels of the image can be combined to obtain a compound visual object. the unsupervised machine learning classification model can be augmented to model the image as a mixture of subjects, where each subject is represented through placements of the visual objects in a mixture of concentric spheres centering on a mixture of intersections on a mixture of horizontal layers. at least one processor can learn latent relationships between the placements of the visual objects in a three-dimensional space depicted in the image and image semantics. learning the latent relationships trains the unsupervised machine learning classification model to perform image subject classification through the placements of the visual objects in a new image.
