V.N. Alexandrov et al. (Eds.): ICCS 2006, Part III, LNCS 3993, pp. 232 – 239, 2006. © Springer-Verlag Berlin Heidelberg 2006 A Novel Approach for Similarity Measure Schemes Based on Multiple Moving Objects in Video Databases Choon-Bo Shim 1 , Chang-Sun Shin 1 , DongGook Park 1 , and Won-Ho So 2 1 School of Information & Communication Engineering 2 Dept. of Computer Education, Sunchon National University, Sunchon, Jeonnam 540-742, South Korea {cbsim, csshin, dgpark, whso}@sunchon.ac.kr Abstract. The general aim of this paper is to study the spatio-temporal modeling techniques which can efficiently represent multiple moving objects' in video databases. The traditional schemes only consider direction property, time interval property, and spatial relationship property for modeling moving objects' trajectories. But, our scheme also takes into account on distance property, conceptual location information, and related object information so that we may improve a retrieval accuracy to measure a similarity between two moving objects as well as them. As its application, we implement the Content- and Semantic-based Soccer Video Retrieval (CS 2 VR) system by using MS Visual C++ and DirectX for indexing and searching on soccer video data. The CS 2 VR helps users to easily extract the trajectory information of soccer ball form soccer video data semi-automatically as well as to conveniently retrieve the results acquired by sketching query trajectory with mouse button. 1 Introduction The initial research issues on the content-based video retrieval have highly concentrated on data representation schemes which can efficiently model content itself extracted from video data [1-5]. However, for handling a large amount of multimedia data, it is required to provide schemes with good retrieval performance on a variety of user queries. Thus, we propose a new spatio-temporal modeling technique which can efficiently represent multiple moving objects in video databases. The traditional schemes only consider direction property, time interval property, and spatial relationship property for modeling moving objects' trajectories. However, our scheme also takes into account on distance property, conceptual location information, and related object information (e.g. player name having a soccer ball) so that we may improve retrieval accuracy to measure a similarity between two moving objects as well as them. Therefore, the proposed spatio-temporal scheme can support content- based retrieval using moving objects' trajectories as well as semantics-based retrieval using concepts which are acquired through the co nceptual location information of moving objects. As its application, we design and implement the Content- and Semantic-based Soccer Video Retrieval (CS 2 VR) system. Finally, in our performance study, our scheme yields substantially better retrieval performance compared to existing related work in term of retrieval effectiveness.