Asymmetric Feature Maps with Application to Sketch Based Retrieval

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F17%3A00312201" target="_blank" >RIV/68407700:21230/17:00312201 - isvavai.cz</a>

Result on the web

<a href="http://dx.doi.org/10.1109/CVPR.2017.655" target="_blank" >http://dx.doi.org/10.1109/CVPR.2017.655</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/CVPR.2017.655" target="_blank" >10.1109/CVPR.2017.655</a>

Result language

angličtina

Original language name

Asymmetric Feature Maps with Application to Sketch Based Retrieval

Original language description

We propose a novel concept of asymmetric feature maps (AFM), which allows to evaluate multiple kernels between a query and database entries without increasing the memory requirements. To demonstrate the advantages of the AFM method, we derive a short vector image representation that, due to asymmetric feature maps, supports efficient scale and translation invariant sketch-based image retrieval. Unlike most of the short-code based retrieval systems, the proposed method provides the query localization in the retrieved image. The efficiency of the search is boosted by approximating a 2D translation search via trigonometric polynomial of scores by 1D projections. The projections are a special case of AFM. An order of magnitude speed-up is achieved compared to traditional trigonometric polynomials. The results are boosted by an image-based average query expansion, exceeding significantly the state of the art on standard benchmarks.

Czech name

—

Czech description

—

Type

D - Article in proceedings

CEP classification

—

OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

<a href="/en/project/LL1303" target="_blank" >LL1303: Large Scale Category Retrieval</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2017

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

CVPR 2017: Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition

ISBN

978-1-5386-0457-1

ISSN

1063-6919

e-ISSN

—

Number of pages

9

Pages from-to

6185-6193

Publisher name

IEEE Computer Society Press

Place of publication

—

Event location

Honolulu

Event date

Jul 21, 2017

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

000418371406030