Indexing Images for Visual Memory by Using {DNN} Descriptors -- Preliminary Experiments

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F14%3A00224957" target="_blank" >RIV/68407700:21230/14:00224957 - isvavai.cz</a>
Result on the web
<a href="http://cmp.felk.cvut.cz/pub/cmp/articles/svoboda/Derner-TR-2014-25.pdf" target="_blank" >http://cmp.felk.cvut.cz/pub/cmp/articles/svoboda/Derner-TR-2014-25.pdf</a>
DOI - Digital Object Identifier
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Result language
angličtina
Original language name
Indexing Images for Visual Memory by Using {DNN} Descriptors -- Preliminary Experiments
Original language description
Visual memory in mobile robotics is important to make the local- ization of a robot robust to situations, when GPS or similar localization methods are not available. Unlike many conventional approaches us- ing local features, we use a holistic method that employs deep neural networks (DNNs) to calculate a global descriptor of the whole image. We consider a scenario in which a robot equipped with an omni-directional camera calculates and stores DNN descriptors of images together with the positions as itmoves in the environment. When the position is unknown to the robot, the algorithm estimates it for a given omnidirectional image by matching it with the most similar database image. We compared our approach with a recently tested GIST-based approach onthe same dataset and we found out that the DNN-based approach yields better results. The experiments also show that the DNN-based algorithm is quite robust to partial occlusion, rotation and changes in lighting conditions.
Czech name
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Czech description
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Publication year
2014
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Number of pages
16
Place of publication
Praha
Publisher/client name
Center for Machine Perception, K13133 FEE, Czech Technical University
Version
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