A Multimedia Retrieval Framework Based on Semi-Supervised Ranking and Relevance Feedback

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...

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Veröffentlicht in:IEEE transactions on pattern analysis and machine intelligence 2012-04, Vol.34 (4), p.723-742
Hauptverfasser: Yang, Yi, Nie, Feiping, Xu, Dong, Luo, Jiebo, Zhuang, Yueting, Pan, Yunhe
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container_title IEEE transactions on pattern analysis and machine intelligence
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creator Yang, Yi
Nie, Feiping
Xu, Dong
Luo, Jiebo
Zhuang, Yueting
Pan, Yunhe
description 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.
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User interface</subject><subject>Content-based multimedia retrieval</subject><subject>cross-media retrieval</subject><subject>Data models</subject><subject>Databases, Factual</subject><subject>Exact sciences and technology</subject><subject>Humans</subject><subject>Image Enhancement - methods</subject><subject>Image retrieval</subject><subject>Information Storage and Retrieval - methods</subject><subject>Information systems. Data bases</subject><subject>Learning and adaptive systems</subject><subject>Manifolds</subject><subject>Memory organisation. Data processing</subject><subject>Multimedia - standards</subject><subject>Multimedia communication</subject><subject>Multimedia databases</subject><subject>Pattern Recognition, Automated</subject><subject>Pattern recognition. Digital image processing. 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User interface</topic><topic>Content-based multimedia retrieval</topic><topic>cross-media retrieval</topic><topic>Data models</topic><topic>Databases, Factual</topic><topic>Exact sciences and technology</topic><topic>Humans</topic><topic>Image Enhancement - methods</topic><topic>Image retrieval</topic><topic>Information Storage and Retrieval - methods</topic><topic>Information systems. Data bases</topic><topic>Learning and adaptive systems</topic><topic>Manifolds</topic><topic>Memory organisation. Data processing</topic><topic>Multimedia - standards</topic><topic>Multimedia communication</topic><topic>Multimedia databases</topic><topic>Pattern Recognition, Automated</topic><topic>Pattern recognition. Digital image processing. 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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.</abstract><cop>Los Alamitos, CA</cop><pub>IEEE</pub><pmid>21844624</pmid><doi>10.1109/TPAMI.2011.170</doi><tpages>20</tpages></addata></record>
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subjects 3D motion data retrieval
Algorithm design and analysis
Algorithms
Applied sciences
Artificial intelligence
Computer science
control theory
systems
Computer systems and distributed systems. User interface
Content-based multimedia retrieval
cross-media retrieval
Data models
Databases, Factual
Exact sciences and technology
Humans
Image Enhancement - methods
Image retrieval
Information Storage and Retrieval - methods
Information systems. Data bases
Learning and adaptive systems
Manifolds
Memory organisation. Data processing
Multimedia - standards
Multimedia communication
Multimedia databases
Pattern Recognition, Automated
Pattern recognition. Digital image processing. Computational geometry
Radio frequency
ranking algorithm
relevance feedback
semi-supervised learning
Software
Studies
title A Multimedia Retrieval Framework Based on Semi-Supervised Ranking and Relevance Feedback
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