Fast Discovery of Discriminative Mid-level Patches
Angran Lin, Xuhui Jia, Kowk Ping Chan · 2015
Learning discriminative mid-level patches has gained popularity in recent years since they can be applied to various computer vision topics and achieve better performance. However, state-of-the-art learning methods require a lot of training time, especially when the problem scale becomes much larger. In this paper we propose a simple but fast and effective way, the Fast Exemplar Clustering(FEC), to mine discriminative mid-level patches with only class labels provided. We verified our results on the task of scene classification and it took us only one day to train the model on the MIT Indoor 67 dataset using an Core i5 quad-core computer with Matlab. The results of our experiments revealed that the mid-level patches discovered by our method were semantically meaningful and achieved competitive accuracy compared to the state-of-the-art techniques. In addition, we created a new scene classification dataset named Outdoor Sight 20 which contains outdoor views of 20 famous tourist attractions to test our model.