Learning a semantic space from user's relevance feedback for image retrieval

• Published in: IEEE transactions on circuits and systems for video technology Vol. 13; no. 1; pp. 39 - 48
• Main Authors: Xiaofei He, King, O., Wei-Ying Ma, Mingjing Li, Hong-Jiang Zhang
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.01.2003; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Asia; Circuits; Content based retrieval; Feedback; Image databases; Image retrieval; Learning; Learning systems; Retrieval; Robustness; Semantics; Shape; Singular value decomposition; Spectral methods
• ISSN: 1051-8215; 1558-2205
• DOI: 10.1109/TCSVT.2002.808087

As current methods for content-based retrieval are incapable of capturing the semantics of images, we experiment with using spectral methods to infer a semantic space from user's relevance feedback, so that our system will gradually improve its retrieval performance through accumulated user interactions. In addition to the long-term learning process, we also model the traditional approaches to query refinement using relevance feedback as a short-term learning process. The proposed short- and long-term learning frameworks have been integrated into an image retrieval system. Experimental results on a large collection of images have shown the effectiveness and robustness of our proposed algorithms.