HFAG: Hierarchical Frame Affinity Group for video retrieval on very large video dataset

Abstract

Content-based video retrieval systems are desired to fast and accurately find the nearest-neighbors of user input examples from very large video datasets. This poses a great challenge since exhaustive and redundant computation of similarities is required. Cluster based index approaches can be used to address this problem, but the similarity computation and clustering methods for videos are very time-consuming, thus preventing it from indexing very large video datasets. In this paper, we propose the Hierarchical Frame Affinity Group (HFAG), which is a hierarchy of frame clusters built using affinity propagation (AP) method, to represent video clusters. Our proposed video similarity metric and AP method guarantee the high performance of forming HFAG. We then build the cluster-based index structure to support retrieval of the nearest-neighbors of video sequences. The experiments on real large video datasets prove the effectiveness and efficiency of our approach.