Multicontext-adaptive indexing and search for large-scale video navigation

Many multimedia retrieval tasks are faced with increasingly large-scale datasets and variously changing preferences of users in each query. There are at least three distinctive contextual aspects comprised to form a set of preferences of a user at each query time: content, intention, and response time. A content preference refers to the low-level or semantic representations of the data that a user is interested in. An intention preference refers to how the content should be regarded as relevant. And a response time preference refers to the ability to control a reasonable wait time. This paper features the dynamic adaptability of a multimedia search system to the contexts of its users and proposes a multicontext-adaptive indexing and search system for video data. The main contribution is the integration of context-based query creation functions with high-performance search algorithms into a unified search system. The indexing method modifies inverted list data structure in order to construct disk-resident databases for large-scale data and efficiently enables a dynamic pruning search mechanism on those indices. We implement a frame-wise video navigation system as an application of the indexing and search system using the a 2.14 TB movie dataset. Experimental studies on this system show the effectiveness of the proposed pruning search method when dealing with dynamic contexts and its comparative high search performance.