Topic
Artificial imagination
About: Artificial imagination is a research topic. Over the lifetime, 17 publications have been published within this topic receiving 58 citations.
Papers
3 Nov 2010
TL;DR: A novel paradigm called ‘artificial imagination’ is proposed, which gives the retrieval system the power to imagine and think along with the user in terms of what she is looking for, and a new user interface for visualizing and exploring image collections is introduced.
Abstract: In my dissertation I investigate techniques for improving the state of the art in content-based image retrieval. To place my work into context, I highlight the current trends and challenges in my field by analyzing over 200 recent articles. Next, I propose a novel paradigm called ‘artificial imagination’, which gives the retrieval system the power to imagine and think along with the user in terms of what she is looking for. I then introduce a new user interface for visualizing and exploring image collections, empowering the user to navigate large collections based on her own needs and preferences, while simultaneously providing her with an accurate sense of what the database has to offer. In the later chapters I present work dealing with millions of images and focus in particular on high-performance techniques that minimize memory and computational use for both near-duplicate image detection and web search. Finally, I show early work on a scene completion-based image retrieval engine, which synthesizes realistic imagery that matches what the user has in mind.
21 citations
1 Jan 2016
TL;DR: In this article, an Artificial Intelli- gence system using deep convolutional neural network (ConvNet) will be able to “imagine” architecture, which can be affiliated to the research field of generative archi- tecture.
Abstract: This paper attempts to determine if an Artificial Intelli- gence system using deep convolutional neural network (ConvNet) will be able to “imagine” architecture. Imagining architecture by means of algorithms can be affiliated to the research field of generative archi- tecture. ConvNet makes it possible to avoid that difficulty by automat- ically extracting and classifying these rules as features from large ex- ample data. Moreover, image-base rendering algorithms can manipu- late those abstract rules encoded in the ConvNet. From these rules and without constructing a prior 3D model, these algorithms can generate perspective of an architectural image. To conclude, establishing shape grammar with this automated system opens prospects for generative architecture with image-base rendering algorithms.
15 citations
1 Jan 2016
TL;DR: To conclude, establishing shape grammar with this automated system opens prospects for generative architecture with image-base rendering algorithms.
Abstract: This paper attempts to determine if an Artificial Intelli- gence system using deep convolutional neural network (ConvNet) will be able to “imagine” architecture. Imagining architecture by means of algorithms can be affiliated to the research field of generative archi- tecture. ConvNet makes it possible to avoid that difficulty by automat- ically extracting and classifying these rules as features from large ex- ample data. Moreover, image-base rendering algorithms can manipu- late those abstract rules encoded in the ConvNet. From these rules and without constructing a prior 3D model, these algorithms can generate perspective of an architectural image. To conclude, establishing shape grammar with this automated system opens prospects for generative architecture with image-base rendering algorithms.
12 citations
1 Dec 2008
TL;DR: A novel approach for using artificially imagined images in relevance feedback since the search engine constructs the synthetic images itself and any feedback given by the user on these images allows it to obtain a better understanding of what the user is looking for than it would from feedback on database images alone.
Abstract: Our goal is to determine if artificially imagined or synthesized images can be beneficial to interactive visual search We present a novel approach for using artificially imagined images in relevance feedback Since the search engine constructs the synthetic images itself, any feedback given by the user on these images allows it to obtain a better understanding of what the user is looking for than it would from feedback on database images alone We evaluated and compared our image synthesis approach with a normal Rocchio-based system on a well-known texture database with real users
9 citations
9 Jul 2007
TL;DR: This project investigates the promising content-based retrieval paradigm known as interactive search or relevance feedback, and aims to extend it through the use of synthetic imagery through a new fundamental paradigm: Artificial Imagination (AIm).
Abstract: In this project (VIRSI) we investigate the promising content-based retrieval paradigm known as interactive search or relevance feedback, and aim to extend it through the use of synthetic imagery. In relevance feedback methods, the user himself is a key factor in the search process as he provides positive and negative feedback on the results, which the system uses to iteratively improve the set of candidate results. In our approach we closely integrate the generation of synthetic imagery in the relevance feedback process through a new fundamental paradigm: Artificial Imagination (AIm).
7 citations
Related Topics (5)
Performance Metrics
| Year | Papers |
|---|---|
| 2021 | 1 |
| 2019 | 6 |
| 2018 | 1 |
| 2016 | 2 |
| 2013 | 2 |
| 2010 | 1 |