6533b82dfe1ef96bd129134b
RESEARCH PRODUCT
LogDet divergence-based metric learning with triplet constraints and its applications.
Hamid Reza KarimiHuijun GaoJiangyuan MeiMeizhu Liusubject
AutomatedData InterpretationBiometryFeature extractionhigh dimensional datametric learningPattern RecognitionFacial recognition systemSensitivity and SpecificityMatrix decompositionPattern Recognition Automatedcompressed representationComputer-AssistedArtificial Intelligencecompressed representation; high dimensional data; LogDet divergence; metric learning; triplet constraint; Artificial Intelligence; Biometry; Data Interpretation Statistical; Face; Humans; Image Enhancement; Image Interpretation Computer-Assisted; Pattern Recognition Automated; Photography; Reproducibility of Results; Sensitivity and Specificity; Algorithms; Facial Expression; Software; Medicine (all); Computer Graphics and Computer-Aided DesignImage Interpretation Computer-AssistedPhotographyHumansDivergence (statistics)Image retrievalImage InterpretationMathematicsMahalanobis distancebusiness.industryLogDet divergenceMedicine (all)Reproducibility of ResultsPattern recognitionStatisticalImage EnhancementComputer Graphics and Computer-Aided DesignFacial ExpressionComputingMethodologies_PATTERNRECOGNITIONComputer Science::Computer Vision and Pattern RecognitionData Interpretation StatisticalFaceMetric (mathematics)Pattern recognition (psychology)Artificial intelligencetriplet constraintbusinessSoftwareAlgorithmsdescription
How to select and weigh features has always been a difficult problem in many image processing and pattern recognition applications. A data-dependent distance measure can address this problem to a certain extent, and therefore an accurate and efficient metric learning becomes necessary. In this paper, we propose a LogDet divergence-based metric learning with triplet constraints (LDMLT) approach, which can learn Mahalanobis distance metric accurately and efficiently. First of all, we demonstrate the good properties of triplet constraints and apply it in LogDet divergence-based metric learning model. Then, to deal with high-dimensional data, we apply a compressed representation method to learn, store, and evaluate Mahalanobis matrix efficiently. Besides, a dynamic triplets building strategy is proposed to build a feedback from the obtained Mahalanobis matrix to the triplet constraints, which can further improve the LDMLT algorithm. Furthermore, the proposed method is applied to various applications, including pattern recognition, facial expression recognition, and image retrieval. The results demonstrate the improved performance of the proposed approach.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2014-09-30 | IEEE transactions on image processing : a publication of the IEEE Signal Processing Society |