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

被引：0

作者：

Zagoris, Konstantinos ^{[1
]}

Chatzichristofis, Savvas A. ^{[1
]}

Arampatzis, Avi ^{[1
]}

机构：

[1] Democritus Univ Thrace, Dept Elect & Comp Engn, Xanthi 67100, Greece

来源：

PROCEEDINGS OF THE 34TH INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL (SIGIR'11) | 2011年

关键词：

Image Retrieval; Bag-Of-Visual-Words; Two-Stage Retrieval;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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 enhanced 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.

引用

页码：1251 / 1252

页数：2

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