Large-scale indoor/outdoor image classification via Expert Decision Fusion (EDF)

Abstract

In this work, we propose an Expert Decision Fusion (EDF) system to tackle the large-scale indoor/outdoor image classification problem using two key ideas, namely, data grouping and decision stacking. By data grouping, we partition the entire data space into multiple disjoint sub-spaces so that a more accurate prediction model can be trained in each sub-space. After data grouping, the EDF system integrates soft decisions from multiple classifiers (called experts here) through stacking so that multiple experts can compensate each other’s weakness. The EDF system offers more accurate and robust classification performance since it can handle data diversity effectively while benefiting from data abundance in large-scale datasets. The advantages of data grouping and decision stacking are explained and demonstrated in detail. We conduct experiments on the SUN dataset and show that the EDF system outperforms all existing methods by a significant margin with a correct classification rate of 91%.