D 2012

Visual Image Search: Feature Signatures or/and Global Descriptors

LOKOČ, Jakub; David NOVÁK; Michal BATKO and Tomáš SKOPAL

Basic information

Original name

Visual Image Search: Feature Signatures or/and Global Descriptors

Authors

LOKOČ, Jakub; David NOVÁK; Michal BATKO and Tomáš SKOPAL

Edition

Berlin / Heidelberg, Similarity Search and Applications, p. 177-191, 15 pp. 2012

Publisher

Springer

Other information

Language

English

Type of outcome

Proceedings paper

Field of Study

Informatics

Country of publisher

Germany

Confidentiality degree

is not subject to a state or trade secret

Publication form

printed version "print"

References:

Marked to be transferred to RIV

Yes

RIV identification code

RIV/00216224:14330/12:00057558

Organization

Fakulta informatiky – Repository – Repository

ISBN

978-3-642-32152-8

ISSN

Keywords in English

similarity search; CBIR; global visual descriptors; visual signatures; SQFD

Links

GAP103/10/0886, research and development project. GPP202/10/P220, research and development project.
Changed: 1/9/2020 12:47, RNDr. Daniel Jakubík

Abstract

In the original language

The success of content-based retrieval systems stands or falls with the quality of the utilized similarity model. In the case of having no additional keywords or annotations provided with the multimedia data, the hard task is to guarantee the highest possible retrieval precision using only content-based retrieval techniques. In this paper we push the visual image search a step further by testing effective combination of two orthogonal approaches – the MPEG-7 global visual descriptors and the feature signatures equipped by the Signature Quadratic Form Distance. We investigate various ways of descriptor combinations and evaluate the overall effectiveness of the search on three different image collections. Moreover, we introduce a new image collection, TWIC, designed as a larger realistic image collection providing ground truth. In all the experiments, the combination of descriptors proved its superior performance on all tested collections. Furthermore, we propose a re-ranking variant guaranteeing efficient yet effective image retrieval.

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