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Title: A multimedia retrieval framework based on semi-supervised ranking and relevance feedback
Authors: Yang, Yi
Nie, Feiping
Xu, Dong
Luo, Jiebo
Zhuang, Yueting
Pan, Yunhe
Keywords: DRNTU::Engineering::Computer science and engineering
Issue Date: 2012
Source: Yang, Y., Nie, F., Xu, D., Luo, J., Zhuang, Y. & Pan, Y. (2012). A Multimedia Retrieval Framework Based on Semi-Supervised Ranking and Relevance Feedback. IEEE Transactions on Pattern Analysis and Machine Intelligence, 34(4), 723-742.
Series/Report no.: IEEE transactions on pattern analysis and machine intelligence
Abstract: We present a new framework for multimedia content analysis and retrieval which consists of two independent algorithms. First, we propose a new semi-supervised algorithm called ranking with Local Regression and Global Alignment (LRGA) to learn a robust Laplacian matrix for data ranking. In LRGA, for each data point, a local linear regression model is used to predict the ranking scores of its neighboring points. A unified objective function is then proposed to globally align the local models from all the data points so that an optimal ranking score can be assigned to each data point. Second, we propose a semi-supervised long-term Relevance Feedback (RF) algorithm to refine the multimedia data representation. The proposed long-term RF algorithm utilizes both the multimedia data distribution in multimedia feature space and the history RF information provided by users. A trace ratio optimization problem is then formulated and solved by an efficient algorithm. The algorithms have been applied to several content-based multimedia retrieval applications, including cross-media retrieval, image retrieval, and 3D motion/pose data retrieval. Comprehensive experiments on four data sets have demonstrated its advantages in precision, robustness, scalability, and computational efficiency.
ISSN: 0162-8828
DOI: 10.1109/TPAMI.2011.170
Rights: © 2012 IEEE
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:SCSE Journal Articles

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