Publicação
Image Stream Similarity Search in GPU Clusters
| Resumo: | Images are an important part of today’s society. They are everywhere on the internet and computing, from news articles to diverse areas such as medicine, autonomous vehicles and social media. This enormous amount of images requires massive amounts of processing power to process, upload, download and search for images. The ability to search an image, and find similar images in a library of millions of others empowers users with great advantages. Different fields have different constraints, but all benefit from the quick processing that can be achieved. Problems arise when creating a solution for this. The similarity calculation between several images, performing thousands of comparisons every second, is a challenge. The results of such computations are very large, and pose a challenge when attempting to process. Solutions for these problems often take advantage of graphs in order to index images and their similarity. The graph can then be used for the querying process. Creating and processing such a graph in an acceptable time frame poses yet another challenge. In order to tackle these challenges, we take advantage of a cluster of machines equipped with Graphics Processing Units (GPUs), enabling us to parallelize the process of describing an image visually and finding other images similar to it in an acceptable time frame. GPUs are incredibly efficient at processing data such as images and graphs, through algorithms that are heavily parallelizable. We propose a scalable and modular system that takes advantage of GPUs, distributed computing and fine-grained parallellism to detect image features, index images in a graph and allow users to search for similar images. The solution we propose is able to compare up to 5000 images every second. It is also able to query a graph with thousands of nodes and millions of edges in a matter of milliseconds, achieving a very efficient query speed. The modularity of our solution allows the interchangeability of algorithms and different steps in the solution, which provides great adaptability to any needs. |
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| Autores principais: | Azevedo, José Maria Pantoja Mata Vale e |
| Assunto: | computer vision distributed computing graphics processing unit stream processing computer systems and networks high-performance computing |
| Ano: | 2018 |
| País: | Portugal |
| Tipo de documento: | dissertação de mestrado |
| Tipo de acesso: | acesso aberto |
| Instituição associada: | Universidade Nova de Lisboa |
| Idioma: | inglês |
| Origem: | Repositório Institucional da UNL |
| Resumo: | Images are an important part of today’s society. They are everywhere on the internet and computing, from news articles to diverse areas such as medicine, autonomous vehicles and social media. This enormous amount of images requires massive amounts of processing power to process, upload, download and search for images. The ability to search an image, and find similar images in a library of millions of others empowers users with great advantages. Different fields have different constraints, but all benefit from the quick processing that can be achieved. Problems arise when creating a solution for this. The similarity calculation between several images, performing thousands of comparisons every second, is a challenge. The results of such computations are very large, and pose a challenge when attempting to process. Solutions for these problems often take advantage of graphs in order to index images and their similarity. The graph can then be used for the querying process. Creating and processing such a graph in an acceptable time frame poses yet another challenge. In order to tackle these challenges, we take advantage of a cluster of machines equipped with Graphics Processing Units (GPUs), enabling us to parallelize the process of describing an image visually and finding other images similar to it in an acceptable time frame. GPUs are incredibly efficient at processing data such as images and graphs, through algorithms that are heavily parallelizable. We propose a scalable and modular system that takes advantage of GPUs, distributed computing and fine-grained parallellism to detect image features, index images in a graph and allow users to search for similar images. The solution we propose is able to compare up to 5000 images every second. It is also able to query a graph with thousands of nodes and millions of edges in a matter of milliseconds, achieving a very efficient query speed. The modularity of our solution allows the interchangeability of algorithms and different steps in the solution, which provides great adaptability to any needs. |
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