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Bag-of-Visual-Words vs Global Image Descriptors on Two-Stage Multimodal Retrieval
 

Summary: Bag-of-Visual-Words vs Global Image Descriptors
on Two-Stage Multimodal Retrieval
Konstantinos Zagoris Savvas A. Chatzichristofis Avi Arampatzis
Department of Electrical and Computer Engineering
Democritus University of Thrace, Xanthi 67100, Greece
{kzagoris,schatzic,avi}@ee.duth.gr
ABSTRACT
The Bag-Of-Visual-Words (BOVW) paradigm is fast becoming a
popular image representation for Content-Based Image Retrieval
(CBIR), mainly because of its better retrieval effectiveness over
global feature representations on collections with images being near-
duplicate to queries. In this experimental study we demonstrate that
this advantage of BOVW is diminished when visual diversity is en-
hanced by using a secondary modality, such as text, to pre-filter
images. The TOP-SURF descriptor is evaluated against Compact
Composite Descriptors on a two-stage image retrieval setup, which
first uses a text modality to rank the collection and then perform
CBIR only on the top-K items.
Categories and Subject Descriptors: H.3.3 [Information Storage and
Retrieval]: Information Search and Retrieval--retrieval models, search

  

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

 

Collections: Computer Technologies and Information Sciences