Emotion-driven Chinese folk music-image retrieval based on DE-SVM

In this study, we attempt to explore cross-media retrieval between music and image data based on the emotional correlation. Emotion feature analytic could be the bridge of cross-media retrieval, since emotion represents the user׳s perspective and effectively meets the user׳s retrieval need. Currently, there is little research about the emotion correlation of different multimedia data (e.g. image or music). We propose a promising model based on Differential Evolutionary-Support Vector Machine (DE-SVM) to build up the emotion-driven cross-media retrieval system between Chinese folk image and Chinese folk music. In this work, we first build up the Chinese Folk Music Library and Chinese Folk Image Library.Second, we compare Back Propagation(BP), Linear Regression(LR) and Differential Evolutionary-Support Vector Machine (DE-SVM), and find that DE-SVM has the best performance. Then we conduct DE-SVM to build the optimal model for music/image emotion recognition. Finally, an Emotion-driven Chinese Folk Music-Image Exploring System based on DE-SVM is developed and experiment results show our method is effective in terms of retrieval performance.