Image Set Classification Using Multi-layer Multiple Instance Learning with Application to Cannabis Website Classification

Abstract

We propose using multi-layer multiple instance learning (MMIL) for image set classification and applying it to the task of cannabis website classification. We treat each image as an instance in an image set, then each image is further viewed as containing instances of local image patches. This representation naturally extends traditional multiple instance learning (MIL) to multi-layers. We then show that, when using the set kernels for all layers, an MMIL problem can be flattened to a simple one-layer MIL. This flattening, when combined with quantized local image patch representation, drastically improves the computational efficiency by two orders. The flattened set kernel is further improved by weighted codewords and an exponential kernel. The proposed approach is applied to a cannabis website classification task, in which we collected a dataset containing more than 220,000 images from 600 websites. In the experiments our approach compares favorably with several state-of-the-art methods.