Choon-Bo Shim’s research while affiliated with Catholic University of Pusan and other places

Recently, video data has attracted many interest. That is the reason why efficient indexing schemes are required to support the content-based retrieval of video data. But most indexing schemes are not suitable for indexing a high-dimensional data except Hybrid Spill-Tree. In this paper, we propose an efficient high-dimensional indexing scheme to support the content-based retrieval of video data. For this, we extend Hybrid Spill-Tree by using a newly designed clustering technique and by adopting a signature method. Finally, we show that proposed signature-based high dimensional indexing scheme achieves better retrieval performance than existing M-Tree and Hybrid Spill-Tree.

Recently, there have been researches on storage and retrieval technique of moving objects, which are highly concerned by user in database application area such as video databases, spatio-temporal databases, and mobile databases. In this paper, we propose a new signature-based indexing scheme which supports similar sub-trajectory retrieval at well as good retrieval performance on moving objects trajectories. Our signature-based indexing scheme is classified into concatenated signature-based indexing scheme for similar sub-trajectory retrieval, entitled CISR scheme and superimposed signature-based indexing scheme for similar sub-trajectory retrieval, entitled SISR scheme according to generation method of trajectory signature based on trajectory data of moving object. Our indexing scheme can improve retrieval performance by reducing a large number of disk access on data file because it first scans all signatures and does filtering before accessing the data file. In addition, we can encourage retrieval efficiency by appling k-warping algorithm to measure the similarity between query trajectory and data trajectory. Final]y, we evaluate the performance on sequential scan method(SeqScan), CISR scheme, and SISR scheme in terms of data insertion time, retrieval time, and storage overhead. We show from our experimental results that both CISR scheme and SISR scheme are better than sequential scan in terms of retrieval performance and SISR scheme is especially superior to the CISR scheme.

In this paper, we present a content-based video retrieval using moving objects' trajectories. For this, we first present a spatio-temporal representation scheme for modeling moving objects. Secondly, we describe an efficient similar sub-trajectory retrieval scheme based on a new k-warping distance algorithm. To show the efficiency of our similar sub-trajectory retrieval scheme, we perform the performance comparison of our scheme with the existing sub-trajectory retrieval schemes.

Moving objects' trajectories play an important role in doing efficient retrieval in spatial-temporal databases. In this paper, we propose a spatio-temporal representation scheme for modeling the trajectory of moving objects. Our spatio-temporal representation scheme effectively describes not only the single trajectory of a moving object but also the multiple trajectories of two or more moving objects. For measuring similarity between two trajectories, we propose a new k-warping distance algorithm which enhances the existing time warping distance algorithm by permitting up to k replications for an arbitrary motion of a query trajectory. Our k-warping distance algorithm provides an approximate matching between two trajectories as well as an exact matching between them. Based on our k-warping distance algorithm, we also present a similarity measure scheme for both the single trajectory and the multiple trajectories in spatio-temporal databases. Finally, we show from our experiment that our similarity measure scheme based on the k-warping distance outperforms Li's one (no-warping) and Shan's one (infinite-warping) in terms of precision and recall measures.