Local feature embedding for supervised image classification

Local feature embedding considers two constraints: intra-image spatial and inter-image feature affinity in the embedding process. However, it does not work well for the image classification task when the images are with intra-class variation, background clutter, etc. In this paper, we enhance the manifold structure by adding the class label of images into the embedding process. Since class labels are used in the training, our method can be considered as supervised. Four constituents are included in our model: feature consistency, spatial consistency, intra-class compactness and inter-class separability. With the defined Hausdorff distance between two images, different classifiers are exploited for classification. Extensive experiments on seven datasets demonstrate the effectiveness of our proposed image classification model.