Universal image representation based on a multimodal graph
Abstract
A system for classifying a target image with segments having attributes is provided. The system generates a graph for the target image that includes vertices representing segments of the image and edges representing relationships between the connected vertices. For each vertex, the system generates a subgraph that includes the vertex as a home vertex and neighboring vertices representing segments of the target image within a neighborhood of the segment represented by the home vertex. The system applies an autoencoder to each subgraph to generate latent variables to represent the subgraph. The system applies a machine learning algorithm to a feature vector comprising a universal image representation of the target image that is derived from the generated latent variables of the subgraphs to generate a classification for the target image.
- Inventors:
- Issue Date:
- Research Org.:
- Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)
- Sponsoring Org.:
- USDOE
- OSTI Identifier:
- 2222186
- Patent Number(s):
- 11735311
- Application Number:
- 17/470,331
- Assignee:
- Lawrence Livermore National Security, LLC (Livermore, CA)
- DOE Contract Number:
- AC52-07NA27344
- Resource Type:
- Patent
- Resource Relation:
- Patent File Date: 09/09/2021
- Country of Publication:
- United States
- Language:
- English
Citation Formats
Bremer, Peer-Timo, Anirudh, Rushil, and Thiagarajan, Jayaraman Jayaraman. Universal image representation based on a multimodal graph. United States: N. p., 2023.
Web.
Bremer, Peer-Timo, Anirudh, Rushil, & Thiagarajan, Jayaraman Jayaraman. Universal image representation based on a multimodal graph. United States.
Bremer, Peer-Timo, Anirudh, Rushil, and Thiagarajan, Jayaraman Jayaraman. Tue .
"Universal image representation based on a multimodal graph". United States. https://www.osti.gov/servlets/purl/2222186.
@article{osti_2222186,
title = {Universal image representation based on a multimodal graph},
author = {Bremer, Peer-Timo and Anirudh, Rushil and Thiagarajan, Jayaraman Jayaraman},
abstractNote = {A system for classifying a target image with segments having attributes is provided. The system generates a graph for the target image that includes vertices representing segments of the image and edges representing relationships between the connected vertices. For each vertex, the system generates a subgraph that includes the vertex as a home vertex and neighboring vertices representing segments of the target image within a neighborhood of the segment represented by the home vertex. The system applies an autoencoder to each subgraph to generate latent variables to represent the subgraph. The system applies a machine learning algorithm to a feature vector comprising a universal image representation of the target image that is derived from the generated latent variables of the subgraphs to generate a classification for the target image.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2023},
month = {8}
}
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