Video search has become a compelling research topic in recent years, due to the proliferation of online video uploading/sharing sites and the exponential explosion of video data. In this demonstration, we showcase a Web-based integrated platform which performs online detection of near-duplicate occurrences over continuous video streams, as well as retrieval of near-duplicate clips from segmented video collections. In particular, our method to detect relevant subsequences in a streaming video is characterized by a novel one-dimensional distance trajectory capturing the changes of consecutive frames. Such a trajectory is further represented by a sequence of compact signatures. An effective similarity measure is devised to compare the trajectory with multiple query videos. This system shows a number of new features compared with our previous prototype.

Abstract When people realized that relational databases fall short in supporting advanced applications including multimedia data management due to the limited modeling power of the relational data model, researchers went ahead with devising semantic, object‐oriented data models in the 1980s (until early 1990s). While the later commercial development of database systems has led to the so‐called object‐relational databases since late 1990s, such a marriage of the two does not actually solve the problems encountered by multimedia data management. In this paper, we present a new semantic multimedia database model based on an extension to the traditional ANSI/SPARC three‐level architecture, attempting to cater for the unique requirements of multimedia data management. Various facilities of this semantic multimedia database model are described and discussed. A number of applications have been developed based on this new model, and in this paper we shall describe some of these including recipe modeling and graph mining for retrieval. Copyright © 2008 John Wiley & Sons, Ltd.

Improving the precision of information retrieval has been a challenging issue on Chinese Web. As exemplified by Chinese recipes on the Web, it is not easy/natural for people to use keywords (e.g. recipe names) to search recipes, since the names can be literally so abstract that they do not bear much, if any, information on the underlying ingredients or cooking methods. In this paper, we investigate the underlying features of Chinese recipes, and based on workflow-like cooking procedures, we model recipes as graphs. We further propose a novel similarity measurement based on the frequent patterns, and devise an effective filtering algorithm to prune unrelated data so as to support efficient on-line searching. Benefiting from the characteristics of graphs, frequent common patterns can be mined from a cooking graph database. So in our prototype system called RecipeView, we extend the subgraph mining algorithm FSG to cooking graphs and combine it with our proposed similarity measurement, resulting in an approach that well caters for specific users' needs. Our initial experimental studies show that the filtering algorithm can efficiently prune unrelated cooking graphs without affecting the retrieval performance and the similarity measurement gets a relatively higher precision/recall against its counterparts.

Abstract The recent explosive proliferation of interesting and useful data over the Web such as various recipes, while providing people with readily available information, brings out a challenging issue on how to manage such non‐conventional data effectively. To respond to the challenge, we have been developing a Web‐based recipe database system calledDish_Masterto manage recipes in a novel way, which not only covers the static recipe attributes but also elucidates the dynamic cooking behaviors. In this paper, we present several semantic knowledge facilities devised inDish_Master, including a set of semantic modeling and knowledge constructs to effectively represent recipe data, rules and constraints, and user profile aspects. With such a rich set of semantic knowledge facilities,Dish_Masterlays down a solid foundation of providing users with personalized services such as adaptation and recommendation. Users can benefit from the system's real‐time consultation and automatic summarization of cuisine knowledge. The usefulness and elegance ofDish_Masterare demonstrated through an experimental prototype system. Copyright © 2007 John Wiley & Sons, Ltd.

As Flash, a relatively new rich media format, becomes more and more popular on the Web, the genre becomes increasingly important for Flash movie management as a complement to topical principles of classification. Genre classification can identify Flash movies authored in a style most likely to satisfy a user's information need. We present a method for detecting the Flash genre quickly and easily by employing a Bayesian approach. A feature set for representing genre information is proposed and used to build automatic genre classification algorithms. The performance of the proposed approach is evaluated by training a Bayesian classifier on real-world data sets. Classification results from our experiments on thousands of Flash movies demonstrate the usefulness of this approach