TY - JOUR
T1 - Locally adaptive subspace and similarity metric learning for visual data clustering and retrieval
AU - Fu, Yun
AU - Li, Zhu
AU - Huang, Thomas S.
AU - Katsaggelos, Aggelos K.
N1 - This research was funded in part by the Beckman Graduate Fellowship, in part by the US Government VACE program, and in part by the NSF Grant CCF 04-26627. The views and conclusions are those of the authors, not of the US Government or its Agencies.
PY - 2008/6
Y1 - 2008/6
N2 - Subspace and similarity metric learning are important issues for image and video analysis in the scenarios of both computer vision and multimedia fields. Many real-world applications, such as image clustering/labeling and video indexing/retrieval, involve feature space dimensionality reduction as well as feature matching metric learning. However, the loss of information from dimensionality reduction may degrade the accuracy of similarity matching. In practice, such basic conflicting requirements for both feature representation efficiency and similarity matching accuracy need to be appropriately addressed. In the style of "Thinking Globally and Fitting Locally", we develop Locally Embedded Analysis (LEA) based solutions for visual data clustering and retrieval. LEA reveals the essential low-dimensional manifold structure of the data by preserving the local nearest neighbor affinity, and allowing a linear subspace embedding through solving a graph embedded eigenvalue decomposition problem. A visual data clustering algorithm, called Locally Embedded Clustering (LEC), and a local similarity metric learning algorithm for robust video retrieval, called Locally Adaptive Retrieval (LAR), are both designed upon the LEA approach, with variations in local affinity graph modeling. For large size database applications, instead of learning a global metric, we localize the metric learning space with kd-tree partition to localities identified by the indexing process. Simulation results demonstrate the effective performance of proposed solutions in both accuracy and speed aspects.
AB - Subspace and similarity metric learning are important issues for image and video analysis in the scenarios of both computer vision and multimedia fields. Many real-world applications, such as image clustering/labeling and video indexing/retrieval, involve feature space dimensionality reduction as well as feature matching metric learning. However, the loss of information from dimensionality reduction may degrade the accuracy of similarity matching. In practice, such basic conflicting requirements for both feature representation efficiency and similarity matching accuracy need to be appropriately addressed. In the style of "Thinking Globally and Fitting Locally", we develop Locally Embedded Analysis (LEA) based solutions for visual data clustering and retrieval. LEA reveals the essential low-dimensional manifold structure of the data by preserving the local nearest neighbor affinity, and allowing a linear subspace embedding through solving a graph embedded eigenvalue decomposition problem. A visual data clustering algorithm, called Locally Embedded Clustering (LEC), and a local similarity metric learning algorithm for robust video retrieval, called Locally Adaptive Retrieval (LAR), are both designed upon the LEA approach, with variations in local affinity graph modeling. For large size database applications, instead of learning a global metric, we localize the metric learning space with kd-tree partition to localities identified by the indexing process. Simulation results demonstrate the effective performance of proposed solutions in both accuracy and speed aspects.
KW - Dimensionality reduction
KW - Image and video retrieval
KW - Locally adaptive retrieval
KW - Locally embedded analysis
KW - Locally embedded clustering
KW - Manifold
KW - Similarity matching
KW - Subspace learning
KW - Visual clustering
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U2 - 10.1016/j.cviu.2007.09.017
DO - 10.1016/j.cviu.2007.09.017
M3 - Article
AN - SCOPUS:43049168302
SN - 1077-3142
VL - 110
SP - 390
EP - 402
JO - Computer Vision and Image Understanding
JF - Computer Vision and Image Understanding
IS - 3
ER -