Generalized Manifold-Ranking-Based Image Retrieval

• Published in: IEEE transactions on image processing Vol. 15; no. 10; pp. 3170 - 3177
• Main Authors: JINGRUI HE, MINGJING LI, ZHANG, Hong-Jiang, HANGHANG TONG, CHANGSHUI ZHANG
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01-10-2006; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Applied sciences; Content based retrieval; Database Management Systems; Databases, Factual; Exact sciences and technology; Feedback; History; Image converters; Image databases; Image Enhancement - methods; Image Interpretation, Computer-Assisted - methods; Image processing; Image retrieval; Information retrieval; Information Storage and Retrieval - methods; Information theory; Information, signal and communications theory; Learning; Learning systems; manifold ranking; Mathematical analysis; outside the database; Pattern Recognition, Automated - methods; Query processing; relevance feedback; Retrieval; Signal processing; Spreads; Telecommunications and information theory; User-Computer Interface; Vectors (mathematics); Web sites; Learning; State of the art; outside the database; Query; Image retrieval; manifold ranking; relevance feedback; Image databank; Database; Information retrieval
• ISSN: 1057-7149; 1941-0042
• DOI: 10.1109/TIP.2006.877491

In this paper, we propose a general transductive learning framework named generalized manifold-ranking-based image retrieval (gMRBIR) for image retrieval. Comparing with an existing transductive learning method named MRBIR , our method could work well whether or not the query image is in the database; thus, it is more applicable for real applications. Given a query image, gMRBIR first initializes a pseudo seed vector based on neighborhood relationship and then spread its scores via manifold ranking to all the unlabeled images in the database. Furthermore, in gMRBIR, we also make use of relevance feedback and active learning to refine the retrieval result so that it converges to the query concept as fast as possible. Systematic experiments on a general-purpose image database consisting of 5 000 Corel images demonstrate the superiority of gMRBIR over state-of-the-art techniques