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Chapter and Conference Paper
Learning Class-to-Image Distance via Large Margin and L1-Norm Regularization
Image-to-Class (I2C) distance has demonstrated its effectiveness for object recognition in several single-label datasets. However, for the multi-label problem, where an image may contain several regions belong...
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Chapter and Conference Paper
Image-to-Class Distance Metric Learning for Image Classification
Image-To-Class (I2C) distance is first used in Naive-Bayes Nearest-Neighbor (NBNN) classifier for image classification and has successfully handled datasets with large intra-class variances. However, the perfo...
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Chapter and Conference Paper
Kernel Sparse Representation for Image Classification and Face Recognition
Recent research has shown the effectiveness of using sparse coding(Sc) to solve many computer vision problems. Motivated by the fact that kernel trick can capture the nonlinear similarity of features, which ma...
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Chapter and Conference Paper
Motion Context: A New Representation for Human Action Recognition
One of the key challenges in human action recognition from video sequences is how to model an action sufficiently. Therefore, in this paper we propose a novel motion-based representation called Motion Context (MC...