Image Classification by Multi-Instance Learning with Base Sample Selection

摘要

We propose a similarity-based learning style algorithm by regarding each image as a multi-instance (MI) sample for image classification. An image featured as vectorial representation interesting regions is transferred to a MI sample. Then a similarity like matrix is constructed using MI kernel between given images and some carefully selected base images, as the new representation of given images. Three selection strategies are proposed to build the base images set to find an optimal solution. A Weka implementation decision tree is used as the main learner in this paper. Experiments on image data repository ALOI and Corel Image 2000 show the effectiveness of the proposed algorithm compared to some previous based line methods.