Publication Type

Journal Article

Version

acceptedVersion

Publication Date

7-2016

Abstract

System performance assessment and comparison are fundamental for large-scale image search engine development. This article documents a set of comprehensive empirical studies to explore the effects of multiple query evidences on large-scale social image search. The search performance based on the social tags, different kinds of visual features and their combinations are systematically studied and analyzed. To quantify the visual query complexity, a novel quantitative metric is proposed and applied to assess the influences of different visual queries based on their complexity levels. Besides, we also study the effects of automatic text query expansion with social tags using a pseudo relevance feedback method on the retrieval performance. Our analysis of experimental results shows a few key research findings: (1) social tag-based retrieval methods can achieve much better results than content-based retrieval methods; (2) a combination of textual and visual features can significantly and consistently improve the search performance; (3) the complexity of image queries has a strong correlation with retrieval results’ quality— more complex queries lead to poorer search effectiveness; and (4) query expansion based on social tags frequently causes search topic drift and consequently leads to performance degradation.

Keywords

Query evidence, Social image retrieval, Performance, Evaluation, Experimentation

Discipline

Computer Sciences | Databases and Information Systems

Research Areas

Data Science and Engineering

Publication

Multimedia Systems

Volume

22

Issue

4

First Page

509

Last Page

523

ISSN

0942-4962

Identifier

10.1007/s00530-014-0432-7

Publisher

Springer

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1007/s00530-014-0432-7

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