Betweenness Centrality Approaches for Image Retrieval

To quantify social tags´ relatedness in an image collection, we examine the betweenness centrality measure. We depict the image collection as a multi-graph representation, where nodes are the social tags and edges bind an image´s social tags. We present our weighted betweenness centrality algorithm and compare it to the unweighted version on sparse and dense graphs. The MIRFLICKR and ImageCLEF benchmark image collections are used in our experimental evaluation. We notice an 11% increase in the computation runtime with weighted edges in determining shortest paths within our image collections. We discuss the intended impact of our approach in conjunction with a node importance evaluation, via the k-path centrality algorithm, for determining situation-aware path planning applications.