Multi-modal Multi-label Semantic Indexing of Images Based on Hybrid Ensemble Learning

Abstract

Automatic image annotation (AIA) refers to the association of words to whole images which is considered as a promising and effective approach to bridge the semantic gap between low-level visual features and high-level semantic concepts. In this paper, we formulate the task of image annotation as a multi-label multi class semantic image classification problem and propose a simple yet effective method: hybrid ensemble learning framework in which multi-label classifier based on uni-modal features and ensemble classifier based on bi-modal features are integrated into a joint classification model to perform multi-modal multi-label semantic image annotation. We conducted experiments on two commonly-used keyframe and image collections: MediaMill and Scene dataset including about 40,000 examples. The empirical studies demonstrated that the proposed hybrid ensemble learning method can enhance a given weak multi-label classifier to some extent, showing the effectiveness of our proposed method when limited number of multi-labeled training data is available.