Stacked generalisation: a novel solution to bridge the semantic gap for content‐based image retrieval

A two‐stage mapping model (TSMM), which can be thought of as a two‐levels stacked generalisation scheme for image classification, is presented. The model is proposed to bridge the semantic gap between low‐level image features and high‐level concepts in a divide‐and‐conquer manner, and aimed at minimising the gap by reducing classification errors. The idea is to design two level‐0 generalisers to classify colour and texture features into colour and texture concepts respectively. Then, a level‐1 generaliser is designed to classify the colour and texture concepts as middle‐(words)‐level concepts into high‐level conceptual classes.