Facebook5k: A Novel Evaluation Resource Dataset for Cross-Media Search

Semantic concepts selection for model construction and data collection is an open research question. It is highly demanding to choose good multimedia concepts with small semantic gaps to facilitate the work of cross-media system developers. Since, this work is very scarce therefore; this paper contributes a new real-world web image dataset created by NGN Tsinghua Laboratory students for cross media search. Unlike previous datasets, such as Flicker30k, Wikipedia and NUS have high semantic gap, results in leading to inconsistency with real time applications. To overcome these drawbacks, the proposed Facebook5k dataset includes: (1) 5130 images crawled from Facebook through users feelings; (2) Images are categorized according to users feelings; (3) Facebook5k is independent of tags and language, rather than uses feelings for search. Based on the proposed dataset, we point out key features of social website images and identify some research problems on image annotation and retrieval. The benchmark results show the effectiveness of the proposed dataset to simplify and improve general image retrieval.