Multi-modal image retrieval with random walk on multi-layer graphs

The analysis of large collections of image data is still a challenging problem due to the difficulty of capturing the true concepts in visual data. The similarity between images could be computed using different and possibly multimodal features such as color or edge information or even text labels. This motivates the design of image analysis solutions that are able to effectively integrate the multi-view information provided by different feature sets. We therefore propose an algorithm that is able to sort images through a random walk on a multi-layer graph, where each layer corresponds to a different type of information about the image data. We propose an effective method to select the edge weights for the multi-layer graph, such that the image ranking scores are optimised. Our experiments show that the proposed algorithm surpasses state-of-the-art solutions due to a more meaningful image similarity computation.