A PLSA-Based Semantic Bag Generator with Application to Natural Scene Classification under Multi-instance Multi-label Learning Framework

Classifying natural scenes into semantic categories has always been a challenging task. So far, many works in this field are primarily intended for single label classification, where each scene example is represented as a single instance vector. The multi-instance multi-label (MIML) learning framework proposed by Z.H. Zhou et al. provides a new solution to the problem of scene classification in a different way. In this paper, we propose a novel scene classification method based on pLSA-based semantic bag generator and MIML learning framework. Under the framework of MIML learning, we introduce the mechanism that transfers an image into a set of instances through the pLSA-based bag generator. Experiments show that our approach achieves better classification performance comparing with the previous work.