17949527. AUTO-GROUPING GALLERY WITH IMAGE SUBJECT CLASSIFICATION simplified abstract (International Business Machines Corporation)
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 17949527 titled 'AUTO-GROUPING GALLERY WITH IMAGE SUBJECT CLASSIFICATION
Simplified Explanation
The patent application describes a method for enhancing unsupervised machine learning classification models by replacing visual words with visual objects in images. Here are the key points of the innovation:
- A computer processor can replace visual words with visual objects in an image.
- Co-occurring single visual objects in an image can be combined to form compound visual objects.
- The model is augmented to represent the image as a mixture of subjects, each represented by the placements of visual objects in concentric spheres.
- Latent relationships between visual object placements and image semantics are learned to improve image subject classification.
- Potential Applications:**
This technology could be applied in image recognition systems, content analysis tools, and automated tagging systems.
- Problems Solved:**
This innovation helps improve the accuracy and efficiency of unsupervised machine learning classification models for image analysis tasks.
- Benefits:**
The technology enhances the ability to classify image subjects accurately, leading to better performance in various applications such as image search and content organization.
- Potential Commercial Applications:**
"Enhancing Image Classification with Visual Objects: Applications and Benefits"
- Possible Prior Art:**
There may be prior art related to image recognition systems and unsupervised machine learning models in the field of computer vision.
- Unanswered Questions:**
- 1. How does this technology handle complex images with multiple subjects and objects?**
This article does not delve into the specifics of handling complex images with multiple subjects and objects. Further research may be needed to understand the scalability of the technology in such scenarios.
- 2. What are the computational requirements for implementing this technology on a large scale?**
The article does not provide information on the computational resources needed to implement this technology on a large scale. Understanding the infrastructure requirements is crucial for practical applications.
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.
