Efficient Diffusion on Region Manifolds: Recovering Small Objects with Compact CNN Representations

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F17%3A00312200" target="_blank" >RIV/68407700:21230/17:00312200 - isvavai.cz</a>

Result on the web

<a href="http://dx.doi.org/10.1109/CVPR.2017.105" target="_blank" >http://dx.doi.org/10.1109/CVPR.2017.105</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/CVPR.2017.105" target="_blank" >10.1109/CVPR.2017.105</a>

Result language

angličtina

Original language name

Efficient Diffusion on Region Manifolds: Recovering Small Objects with Compact CNN Representations

Original language description

Query expansion is a popular method to improve the quality of image retrieval with both conventional and CNN representations. It has been so far limited to global image similarity. This work focuses on diffusion, a mechanism that captures the image manifold in the feature space. The diffusion is carried out on descriptors of overlapping image regions rather than on a global image descriptor like in previous approaches. An efficient off-line stage allows optional reduction in the number of stored regions. In the on-line stage, the proposed handling of unseen queries in the indexing stage removes additional computation to adjust the precomputed data. We perform diffusion through a sparse linear system solver, yielding practical query times well below one second. Experimentally, we observe a significant boost in performance of image retrieval with compact CNN descriptors on standard benchmarks, especially when the query object covers only a small part of the image. Small objects have been a common failure case of CNN-based retrieval.

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

10

Pages from-to

926-935

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

000418371400098