Local Image Distance Metric Learning

Distance metric learning has been of wide concern for its prominent influence to many methods in machine learning and pattern recognition . The earlier metric learning algorithms mostly focus on finding distance metric from sample vectors. However, when dealing with images, the existing approaches often encounter high computational cost due to huge dimensionality of image vector space and instability in performance caused by distortion effect of image vectorization. This paper proposes a new technique coined Local Image Distance Metric Learning(LIDML) for image distance measurement. LIDML learns an appropriate distance metric for a novel image distance by optimizing local compactness and local separability. In LIDML, the size of distance metric is comparatively small and the model processes a closed-form solution via eigen-decomposition, both of which make LIDML fairly efficient. The experimental results on image classification demonstrate effectiveness of our method, and also show that LIDML can handle expensive computation difficulty case that hardly be handled satisfactorily by previous methods.