Why stop there?: A novel hill climbing based approach towards multimodal classification

Abstract

Past few decades has witnessed a paradigm shift in object classification where individual feature analysis has given way to multimodal solutions. The semantic gap between different modalities such as text and image still continues to be a challenge resulting in significant amount of research and resources being devoted to the same. One crucial aspect of any multimodal task is to combine the identified features appropriately so that an optimal result can be obtained with enhanced accuracy. Although there are quite a few such feature combination techniques in literature, one can observe enough scope for improvement. In this paper, we try to address this problem of optimal feature combination for classification task using Hill Climbing. To overcome the shortcomings of Hill Climbing we propose an improvised version that increases the classification efficiency and accuracy significantly. Thorough experiments on a standard dataset using established metrics substantiate that our proposed method outperforms state-of-the-art feature combination techniques. © 2017 IEEE.