Hidden annotation for image retrieval with long-term relevance feedback learning

Authors:
Wei Jiang;Guihua Er;Qionghai Dai;Jinwei Gu
Affiliations:
Department of Automation, Tsinghua University, Beijing 100084, China and Room 703, Building 32, Tsinghua University, Beijing 100084, China;Department of Automation, Tsinghua University, Beijing 100084, China and Room 520, Main Building, Tsinghua University, Beijing 100084, China;Department of Automation, Tsinghua University, Beijing 100084, China and Room 520, Main Building, Tsinghua University, Beijing 100084, China;Department of Automation, Tsinghua University, Beijing 100084, China and Room 626B, Main Building, Tsinghua University, Beijing 100084, China
Venue:
Pattern Recognition
Year:
2005

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Abstract

Hidden annotation (HA) is an important research issue in content-based image retrieval (CBIR). We propose to incorporate long-term relevance feedback (LRF) with HA to increase both efficiency and retrieval accuracy of CBIR systems. The work contains two parts. (1) Through LRF, a multi-layer semantic representation is built to automatically extract hidden semantic concepts underlying images. HA with these concepts alleviates the burden of manual annotation and avoids the ambiguity problem of keyword-based annotation. (2) For each learned concept, semi-supervised learning is incorporated to automatically select a small number of candidate images for annotators to annotate, which improves efficiency of HA.