Home

About

Advanced Search

Browse by Discipline

Scientific Societies

E-print Alerts

Add E-prints

E-print Network
FAQHELPSITE MAPCONTACT US


  Advanced Search  

 
Dynamic Two-Stage Image Retrieval from Large Multimodal Databases
 

Summary: Dynamic Two-Stage Image Retrieval
from Large Multimodal Databases
Avi Arampatzis, Konstantinos Zagoris, and Savvas A. Chatzichristofis
Department of Electrical and Computer Engineering,
Democritus University of Thrace, Xanthi 67100, Greece
{avi,kzagoris,schatzic}@ee.duth.gr
Abstract. Content-based image retrieval (CBIR) with global features is notori-
ously noisy, especially for image queries with low percentages of relevant images
in a collection. Moreover, CBIR typically ranks the whole collection, which is
inefficient for large databases. We experiment with a method for image retrieval
from multimodal databases, which improves both the effectiveness and efficiency
of traditional CBIR by exploring secondary modalities. We perform retrieval in
a two-stage fashion: first rank by a secondary modality, and then perform CBIR
only on the top-K items. Thus, effectiveness is improved by performing CBIR
on a `better' subset. Using a relatively `cheap' first stage, efficiency is also im-
proved via the fewer CBIR operations performed. Our main novelty is that K is
dynamic, i.e. estimated per query to optimize a predefined effectiveness measure.
We show that such dynamic two-stage setups can be significantly more effective
and robust than similar setups with static thresholds previously proposed.
1 Introduction

  

Source: Arampatzis, Avi - Department of Electrical and Computer Engineering, Democritus University of Thrace

 

Collections: Computer Technologies and Information Sciences