The effects of multiple query evidences on social image retrieval

Zhiyong Cheng, Jialie Shen, Haiyan Miao

Research output: Contribution to journalArticlepeer-review

20 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)509-523
Number of pages15
JournalMultimedia Systems
Issue number4
Early online date19 Dec 2014
Publication statusPublished - 1 Jul 2016
Externally publishedYes


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