17932742. EXPLORATION OF LARGE-SCALE DATA SETS simplified abstract (ADOBE INC.)
Contents
- 1 EXPLORATION OF LARGE-SCALE DATA SETS
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 EXPLORATION OF LARGE-SCALE DATA SETS - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Original Abstract Submitted
EXPLORATION OF LARGE-SCALE DATA SETS
Organization Name
Inventor(s)
Sachin Madhav Kelkar of Santa Clara CA (US)
Ajinkya Gorakhnath Kale of San Jose CA (US)
Alvin Ghouas of Berkeley CA (US)
Baldo Antonio Faieta of San Francisco CA (US)
EXPLORATION OF LARGE-SCALE DATA SETS - A simplified explanation of the abstract
This abstract first appeared for US patent application 17932742 titled 'EXPLORATION OF LARGE-SCALE DATA SETS
Simplified Explanation
The patent application describes systems and methods for image exploration, including identifying a set of images, reducing the set to a representative set by removing neighbor images, arranging the representative set in a grid structure using a self-sorting map algorithm, and displaying a portion of the representative set based on the grid structure.
- Identifying a set of images
- Reducing the set of images to obtain a representative set by removing neighbor images
- Arranging the representative set in a grid structure using a self-sorting map algorithm
- Displaying a portion of the representative set based on the grid structure
Potential Applications
This technology could be applied in image search engines, digital photo albums, and online galleries to help users explore and navigate through large collections of images efficiently.
Problems Solved
This technology solves the problem of overwhelming users with a large number of images by providing a representative set that is easier to navigate and explore.
Benefits
The benefits of this technology include improved user experience, faster image exploration, and more efficient organization of large image datasets.
Potential Commercial Applications
A potential commercial application of this technology could be in e-commerce platforms to enhance product image browsing and selection for customers, leading to increased sales and customer satisfaction.
Possible Prior Art
One possible prior art for this technology could be existing image clustering algorithms used in image search engines or image recognition systems.
What are the specific technical details of the self-sorting map algorithm used in this patent application?
The specific technical details of the self-sorting map algorithm used in this patent application are not provided in the abstract. Further details on the algorithm's implementation, optimization techniques, and performance metrics would be necessary to fully understand its functionality.
How does the removal of neighbor images based on proximity contribute to the efficiency of image exploration in this patent application?
The removal of neighbor images based on proximity helps in reducing redundancy and clustering similar images together in the representative set. This contributes to a more diverse and evenly distributed set of images, making it easier for users to explore different types of images without being overwhelmed by duplicates or similar content.
Original Abstract Submitted
Systems and methods for image exploration are provided. One aspect of the systems and methods includes identifying a set of images; reducing the set of images to obtain a representative set of images that is distributed throughout the set of images by removing a neighbor image based on a proximity of the neighbor image to an image of the representative set of images; arranging the representative set of images in a grid structure using a self-sorting map (SSM) algorithm; and displaying a portion of the representative set of images based on the grid structure.
