Multi-scale and Multi-GMM pooling based on Fisher Kernel for image representation

摘要

Image representation is the key part of image classification, and Fisher kernel has been considered as one of the most effective image feature coding methods. For the Fisher encoding method, there is a critical issue that the single GMM only models features within a rough granularity space. In this paper, we propose a method that is named Multi-scale and Multi-GMM Pooling (MMP), which could effectively represent the image from various granularities. We first conduct pooling using the multi-GMM instead of a single GMM. Then, we introduce multi-scale images to enrich the model's inputs, which could improve the performance further. Finally, we validate out proposal on PASCAL VOC2007 dataset, and the experimental results show an obvious superiority over the basic Fisher model.