Proximity-based Order-respecting Intersection for Searching in Image Databases

HOMOLA, Tomáš, Vlastislav DOHNAL and Pavel ZEZULA. Proximity-based Order-respecting Intersection for Searching in Image Databases. In Marcin Detyniecki, Peter Knees, Andreas Nürnberger, Markus Schedl and Sebastian Stober. 8th International Workshop on Adaptive Multimedia Retrieval, AMR'2010. Linz: Johannes Kepler University. p. 174-188. ISBN 978-3-642-27168-7. doi:10.1007/978-3-642-27169-4_13. 2011.

Basic information

• Original name: Proximity-based Order-respecting Intersection for Searching in Image Databases
• Authors: HOMOLA, Tomáš (203 Czech Republic, belonging to the institution), Vlastislav DOHNAL (203 Czech Republic, guarantor, belonging to the institution) and Pavel ZEZULA (203 Czech Republic, belonging to the institution).
• Edition: Linz, 8th International Workshop on Adaptive Multimedia Retrieval, AMR'2010, p. 174-188, 15 pp. 2011.
• Publisher: Johannes Kepler University

Other information

• Original language: English
• Type of outcome: Proceedings paper
• Field of Study: 10201 Computer sciences, information science, bioinformatics
• Country of publisher: Austria
• Confidentiality degree: is not subject to a state or trade secret
• Publication form: printed version "print"
• Impact factor: Impact factor: 0.402 in 2005
• RIV identification code: RIV/00216224:14330/11:00081752
• Organization unit: Faculty of Informatics
• ISBN: 978-3-642-27168-7
• ISSN: 0302-9743
• Doi: http://dx.doi.org/10.1007/978-3-642-27169-4_13
• UT WoS: 000306440900013
• Keywords in English: proximity based order respecting intersection; sub image search; image database; experimental trials
• Tags: DISA
• Tags: International impact, Reviewed

Abstract

As the volume of non-textual data, such images and other multimedia data, available on Internet is increasing. The issue of identifying data items based on query containment rather than query equality is more and more important. In this paper, we propose a solution to this problem. We assume the local descriptors are extracted from data item, so the aforementioned problem reduces to finding data items of a collection that share as many as possible local descriptors with the query. In particular, we defined a new e-intersection that identifies close (similar) descriptors. Local descriptors usually contain the location of the descriptors in the original data, so the proposed solution takes into account them to increase effectiveness of searching. We evaluate the e-intersection on two real-life image collections using SIFT and SURF local descriptors.

Links

• GAP103/10/0886, research and development project: Name: Vizuální vyhledávání obrázků na Webu (Acronym: VisualWeb)

Investor: Czech Science Foundation, Content-based Image Retrieval on the Web Scale

• GA201/09/0683, research and development project: Name: Vyhledávání v rozsáhlých multimediálních databázích

Investor: Czech Science Foundation, Similarity Searching in Very Large Multimedia Databases

• MUNI/A/0922/2009, interní kód MU: Name: Aplikovaný výzkum Fakulty informatiky (Acronym: AVFI)

Investor: Masaryk University, Category A