Name
Title | ||
|---|---|---|
Locally adaptive subspace and similarity metric learning for visual data clustering and retrieval |
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1016/j.cviu.2007.09.017 | Computer Vision and Image Understanding |
Keywords | Field | DocType |
manifold,lea approach,locally embedded clustering,visual data,locally adaptive retrieval,subspace learning,visual clustering,feature space dimensionality reduction,metric learning,similarity matching,locally embedded analysis,dimensionality reduction,adaptive subspace,local similarity,image and video retrieval,metric learning algorithm,similarity metric learning,feature representation efficiency,metric learning space,graph embedding,nearest neighbor,data clustering,eigenvalue decomposition,kd tree,indexation,process simulation,feature space | k-nearest neighbors algorithm,Similitude,Feature vector,Dimensionality reduction,Pattern recognition,Search engine indexing,Image retrieval,Artificial intelligence,Metric space,Cluster analysis,Machine learning,Mathematics | Journal |
Volume | Issue | ISSN |
110 | 3 | Computer Vision and Image Understanding |
Citations | PageRank | References |
18 | 0.81 | 34 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yun Fu | 1 | 4267 | 208.09 |
| Zhu Li | 2 | 940 | 82.17 |
| Thomas S. Huang | 3 | 27815 | 2618.42 |
| A. K. Katsaggelos | 4 | 2699 | 297.26 |