Maintaining knowledge about temporal intervals
Communications of the ACM
Extended Boolean information retrieval
Communications of the ACM
Learning video browsing behavior and its application in the generation of video previews
MULTIMEDIA '01 Proceedings of the ninth ACM international conference on Multimedia
MPEG-7: Overview of MPEG-7 Description Tools, Part 2
IEEE MultiMedia
Supporting Ranked Boolean Similarity Queries in MARS
IEEE Transactions on Knowledge and Data Engineering
FALCON: Feedback Adaptive Loop for Content-Based Retrieval
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
A Relevance Feedback Architecture for Content-based Multimedia Information Retrieval Systems
CAIVL '97 Proceedings of the 1997 Workshop on Content-Based Access of Image and Video Libraries (CBAIVL '97)
An Optimized Interaction Strategy for Bayesian Relevance Feedback
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Display time as implicit feedback: understanding task effects
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Context-sensitive information retrieval using implicit feedback
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Integrating Relevance Feedback Techniques for Image Retrieval Using Reinforcement Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Content-based multimedia information retrieval: State of the art and challenges
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
Content-based video retrieval: does video's semantic visual feature matter?
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Fundamentals of Database Systems (5th Edition)
Fundamentals of Database Systems (5th Edition)
Personalized retrieval of sports video
Proceedings of the international workshop on Workshop on multimedia information retrieval
A Learned Lexicon-Driven Paradigm for Interactive Video Retrieval
IEEE Transactions on Multimedia
Can High-Level Concepts Fill the Semantic Gap in Video Retrieval? A Case Study With Broadcast News
IEEE Transactions on Multimedia
Batch Nearest Neighbor Search for Video Retrieval
IEEE Transactions on Multimedia
A Novel Framework for Semantic Annotation and Personalized Retrieval of Sports Video
IEEE Transactions on Multimedia
A Multimodal and Multilevel Ranking Scheme for Large-Scale Video Retrieval
IEEE Transactions on Multimedia
Relevance feedback: a power tool for interactive content-based image retrieval
IEEE Transactions on Circuits and Systems for Video Technology
Learning a semantic space from user's relevance feedback for image retrieval
IEEE Transactions on Circuits and Systems for Video Technology
Adaptive video indexing and automatic/semi-automatic relevance feedback
IEEE Transactions on Circuits and Systems for Video Technology
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In spite of significant improvements in video data retrieval, a system has not yet been developed that can adequately respond to a user's query. Typically, the user has to refine the query many times and view query results until eventually the expected videos are retrieved from the database. The complexity of video data and questionable query structuring by the user aggravates the retrieval process. Most previous research in this area has focused on retrieval based on low-level features. Managing imprecise queries using semantic (high-level) content is no easier than queries based on low-level features due to the absence of a proper continuous distance function. We provide a method to help users search for clips and videos of interest in video databases. The video clips are classified as interesting and uninteresting based on user browsing. The attribute values of clips are classified by commonality, presence, and frequency within each of the two groups to be used in computing the relevance of each clip to the user's query. In this paper, we provide an intelligent query structuring system, called I-Quest, to rank clips based on user browsing feedback, where a template generation from the set of interesting and uninteresting sets is impossible or yields poor results.