Přehled o publikaci
2012
Visual Image Search: Feature Signatures or/and Global Descriptors
LOKOČ, Jakub; David NOVÁK; Michal BATKO and Tomáš SKOPALBasic 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.