Predicting image data with the SRS-12 algorithm. Applying random projection-based c-approximate nearest-neighbor queries to image data - Couverture souple
Synopsis
Bachelor Thesis from the year 2015 in the subject Computer Science - Applied, grade: 1.0, University of Heidelberg (Computer Science), language: English, abstract: Determining point similarity is a cornerstone for a wide variety of applications. Whenever data is being compared, the problem can be stated as a matter of comparing vectors. Due to this fundamental importance, research has been conducted in this area for decades. For a long time, even for relatively low dimensions such as for example d = 10, complexity was far beyond of what was considered to be feasible in practice. A common approach is to soften the requirements for the accuracy of the neighbor search and look for points within a certain proximity to the query point instead of searching for exact neighbors. In this thesis we evaluate how well the SRS-12 algorithm works on real-world image data in order to lay the ground for future work such as image prediction. The SRS-12 algorithm is an approach to c-approximate nearest-neighbors and claims to have a tiny index and arbitrary approximation ratio while maintaining good theoretical guarantees. We first implement and verify the algorithm and subsequently examine the quality of its outputs when it is applied to image data by performing block matching. We find that the SRS-12 algorithm is indeed very suitable for processing image data as long as the input images are cut into patches that are sufficiently large. However the parameters of the algorithm have to be tuned carefully because they significantly affect not only the quality of the results, but also the computation time which can easily exceed an exact nearest-neighbor query if the parameters are not set properly. We conclude our experiment with a recommendation for well-working parameter settings and propose approaches to enhance the quality and speed of approximate nearest-neighbor queries on image data made with the SRS-12 algorithm such that it can be used in real-time.
Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.
Résultats de recherche pour Predicting image data with the SRS-12 algorithm. Applying...
Image d'archives
Predicting image data with the SRS-12 algorithm. Applying random projection-based c-approximate nearest-neighbor queries to image data
Vendeur : California Books, Miami, FL, Etats-Unis
Évaluation du vendeur 4 sur 5 étoiles
Etat : New. N° de réf. du vendeur I-9783668033559
Acheter neuf
EUR 36,90
Livraison gratuite
Expédition nationale : Etats-Unis
Expédition nationale : Etats-Unis
Quantité disponible : Plus de 20 disponibles
Image fournie par le vendeur
Predicting image data with the SRS-12 algorithm. Applying random projection-based c-approximate nearest-neighbor queries to image data
impression à la demandeVendeur : BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Allemagne
Évaluation du vendeur 5 sur 5 étoiles
Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Bachelor Thesis from the year 2015 in the subject Computer Science - Applied, grade: 1.0, University of Heidelberg (Computer Science), language: English, abstract: Determining point similarity is a cornerstone for a wide variety of applications. Whenever data is being compared, the problem can be stated as a matter of comparing vectors. Dueto this fundamental importance, research has been conducted in this area for decades. For a long time, even for relatively low dimensions such as for example d = 10, complexity was far beyond of what was considered to be feasible in practice. A common approach is to soften the requirements for the accuracy of the neighbor search and look for points within a certain proximity to the query point instead of searching for exact neighbors.In this thesis we evaluate how well the SRS-12 algorithm works on real-world image data in order to lay the ground for future work such as image prediction. The SRS-12 algorithm is an approach to c-approximate nearest-neighbors and claims to have a tiny index and arbitrary approximation ratio while maintaining good theoretical guarantees.We first implement and verify the algorithm and subsequently examine the quality of its outputs when it is applied to image data by performing block matching. We find that the SRS-12algorithm is indeed very suitable for processing image data as long as the input images are cut into patches that are sufficiently large. However the parameters of the algorithm have to be tuned carefully because they significantly affect not only the quality of the results, but also the computation time which can easily exceed an exact nearest-neighbor query if the parameters are not set properly. We conclude our experiment with a recommendation for well-working parameter settings and propose approaches to enhance the quality and speed of approximate nearest-neighbor queries on image data made with the SRS-12 algorithm such that it can be used in real-time. 48 pp. Englisch. N° de réf. du vendeur 9783668033559
Acheter neuf
EUR 27,95
Expédition à EUR 23
Expédition depuis Allemagne vers Etats-Unis
Expédition depuis Allemagne vers Etats-Unis
Quantité disponible : 2 disponible(s)
Image d'archives
Predicting image data with the SRS-12 algorithm. Applying random projection-based c-approximate nearest-neighbor queries to image data
impression à la demandeVendeur : Majestic Books, Hounslow, Royaume-Uni
Évaluation du vendeur 4 sur 5 étoiles
Etat : New. Print on Demand pp. 48. N° de réf. du vendeur 402011826
Acheter neuf
EUR 56,01
Expédition à EUR 7,65
Expédition depuis Royaume-Uni vers Etats-Unis
Expédition depuis Royaume-Uni vers Etats-Unis
Quantité disponible : 4 disponible(s)
Image fournie par le vendeur
Predicting image data with the SRS-12 algorithm. Applying random projection-based c-approximate nearest-neighbor queries to image data
Edité par
GRIN Verlag, GRIN Verlag Aug 2015, 2015
ISBN 10 : 3668033552
ISBN 13 : 9783668033559
Neuf
Taschenbuch
Vendeur : buchversandmimpf2000, Emtmannsberg, BAYE, Allemagne
Évaluation du vendeur 5 sur 5 étoiles
Taschenbuch. Etat : Neu. This item is printed on demand - Print on Demand Titel. Neuware -Bachelor Thesis from the year 2015 in the subject Computer Science - Applied, grade: 1.0, University of Heidelberg (Computer Science), language: English, abstract: Determining point similarity is a cornerstone for a wide variety of applications. Whenever data is being compared, the problem can be stated as a matter of comparing vectors. Dueto this fundamental importance, research has been conducted in this area for decades. For a long time, even for relatively low dimensions such as for example d = 10, complexity was far beyond of what was considered to be feasible in practice. A common approach is to soften the requirements for the accuracy of the neighbor search and look for points within a certain proximity to the query point instead of searching for exact neighbors. In this thesis we evaluate how well the SRS-12 algorithm works on real-world image data in order to lay the ground for future work such as image prediction. The SRS-12 algorithm is an approach to c-approximate nearest-neighbors and claims to have a tiny index and arbitrary approximation ratio while maintaining good theoretical guarantees. We first implement and verify the algorithm and subsequently examine the quality of its outputs when it is applied to image data by performing block matching. We find that the SRS-12 algorithm is indeed very suitable for processing image data as long as the input images are cut into patches that are sufficiently large. However the parameters of the algorithm have to be tuned carefully because they significantly affect not only the quality of the results, but also the computation time which can easily exceed an exact nearest-neighbor query if the parameters are not set properly. We conclude our experiment with a recommendation for well-working parameter settings and propose approaches to enhance the quality and speed of approximate nearest-neighbor queries on image data made with the SRS-12 algorithm such that it can be used in real-time.Books on Demand GmbH, Überseering 33, 22297 Hamburg 48 pp. Englisch. N° de réf. du vendeur 9783668033559
Acheter neuf
EUR 27,95
Expédition à EUR 60
Expédition depuis Allemagne vers Etats-Unis
Expédition depuis Allemagne vers Etats-Unis
Quantité disponible : 1 disponible(s)
Image fournie par le vendeur
Predicting image data with the SRS-12 algorithm. Applying random projection-based c-approximate nearest-neighbor queries to image data
Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne
Évaluation du vendeur 5 sur 5 étoiles
Taschenbuch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - Bachelor Thesis from the year 2015 in the subject Computer Science - Applied, grade: 1.0, University of Heidelberg (Computer Science), language: English, abstract: Determining point similarity is a cornerstone for a wide variety of applications. Whenever data is being compared, the problem can be stated as a matter of comparing vectors. Dueto this fundamental importance, research has been conducted in this area for decades. For a long time, even for relatively low dimensions such as for example d = 10, complexity was far beyond of what was considered to be feasible in practice. A common approach is to soften the requirements for the accuracy of the neighbor search and look for points within a certain proximity to the query point instead of searching for exact neighbors.In this thesis we evaluate how well the SRS-12 algorithm works on real-world image data in order to lay the ground for future work such as image prediction. The SRS-12 algorithm is an approach to c-approximate nearest-neighbors and claims to have a tiny index and arbitrary approximation ratio while maintaining good theoretical guarantees.We first implement and verify the algorithm and subsequently examine the quality of its outputs when it is applied to image data by performing block matching. We find that the SRS-12algorithm is indeed very suitable for processing image data as long as the input images are cut into patches that are sufficiently large. However the parameters of the algorithm have to be tuned carefully because they significantly affect not only the quality of the results, but also the computation time which can easily exceed an exact nearest-neighbor query if the parameters are not set properly. We conclude our experiment with a recommendation for well-working parameter settings and propose approaches to enhance the quality and speed of approximate nearest-neighbor queries on image data made with the SRS-12 algorithm such that it can be used in real-time. N° de réf. du vendeur 9783668033559
Acheter neuf
EUR 27,95
Expédition à EUR 60,42
Expédition depuis Allemagne vers Etats-Unis
Expédition depuis Allemagne vers Etats-Unis
Quantité disponible : 1 disponible(s)