The rapid increase in generation and dissemination of online video data has recently raised the demand on efficient and effective query processing techniques in large video databases. In this paper, we first introduce a novel compact video representation model to achieve high effectiveness, and then propose to alleviate computational time complexity of the well-known Earth Mover’s Distance by introducing a filter approximation analyzing earth flows locally and restricting the number of flows globally, ensuring completeness .M oreover, extensive experimental evaluation performed on high dimensional real world datasets points out high efficiency and effectiveness of the proposals, significantly reducing the number of Earth Mover’s Distance computations and outperforming the state of the art by up to two orders of magnitude with respect to selectivity and query processing time.

The recent rapid growth of scientific data necessitates efficient similarity search techniques for which convenient object representation models are of vital importance. Feature signatures denoting highly flexible object feature representations have increasingly gained attention for which corresponding efficiency improvement techniques are developed. In this paper, we focus on efficient query processing with the well-known Earth Mover's Distance (EMD) on databases of feature signatures, and propose efficient approximation techniques successfully applicable to high-dimensional feature signatures via dimensionality reduction, guaranteeing both completeness and no false-dismissal within a filter-and-refine architecture. Rigorous experiments on real world data indicate a considerable reduction in the number of EMD computations and high efficiency of the proposed techniques which significantly reduce the query processing time.

The Earth Mover's Distance, proposed in computer vision as a distance-based similarity model reflecting the human perceptual similarity, has been widely utilized in numerous domains for similarity search applicable on both feature histograms and signatures. While efficiency improvement methods towards the Earth Mover's Distance were frequently investigated on feature histograms, not much work is known to study this similarity model on feature signatures denoting object-specific feature representations. Given a very large multimedia database of features signatures, how can k-nearest-neighbor queries be processed efficiently by using the Earth Mover's Distance? In this paper, we propose an efficient filter approximation technique to lower bound the Earth Mover's Distance on feature signatures by restricting the number of earth flows locally. Extensive experiments on real world data indicate the high efficiency of the proposal, attaining order-of-magnitude query processing time cost reduction for high dimensional feature signatures.