Combining the language model and inference network approaches to retrieval
Information Processing and Management: an International Journal - Special issue: Bayesian networks and information retrieval
Learning the semantics of multimedia queries and concepts from a small number of examples
Proceedings of the 13th annual ACM international conference on Multimedia
Large-Scale Concept Ontology for Multimedia
IEEE MultiMedia
Evaluation campaigns and TRECVid
MIR '06 Proceedings of the 8th ACM international workshop on Multimedia information retrieval
Video search in concept subspace: a text-like paradigm
Proceedings of the 6th ACM international conference on Image and video retrieval
Video diver: generic video indexing with diverse features
Proceedings of the international workshop on Workshop on multimedia information retrieval
The importance of query-concept-mapping for automatic video retrieval
Proceedings of the 15th international conference on Multimedia
Semantic concept-based query expansion and re-ranking for multimedia retrieval
Proceedings of the 15th international conference on Multimedia
SURF: speeded up robust features
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
Video retrieval using high level features: exploiting query matching and confidence-based weighting
CIVR'06 Proceedings of the 5th international conference on Image and Video Retrieval
Relay boost fusion for learning rare concepts in multimedia
CIVR'06 Proceedings of the 5th international conference on Image and Video Retrieval
Using high-level semantic features in video retrieval
CIVR'06 Proceedings of the 5th international conference on Image and Video Retrieval
A Learned Lexicon-Driven Paradigm for Interactive 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
Interactive video retrieval with rich features and friendly interface
CIVR '08 Proceedings of the 2008 international conference on Content-based image and video retrieval
Query representation by structured concept threads with application to interactive video retrieval
Journal of Visual Communication and Image Representation
Utilizing related samples to learn complex queries in interactive concept-based video search
Proceedings of the ACM International Conference on Image and Video Retrieval
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Now with a large lexicon of over 300 semantic concepts available for indexing purpose, video retrieval can be made easier by leveraging on the available semantic indices. However, any successful concept-based video retrieval approach must take the following into account: though improving continuously, these concept indexing results are still far from perfect; more concepts are awaiting for detection instead of being detected due to the limited amount of annotated data. If possible, a structured query formulation other than a simple AND logic of some chosen concepts is more desirable to model the complex query need with the fixed concept lexicon. In this paper, we propose a concept-based interactive video retrieval approach to tackle these problems. To better represent the query information need, the proposed approach learns through the feedback information a structured formulation which consists of multiple semantic concept combination terms. Instead of taking the top-ranked items from the selected concepts, it leverages on a simple mining algorithm to drill down to concept-segments where the positive examples are most densely populated than the negative examples. We evaluate the proposed method on the large scale TRECVid 05&06 data sets, and achieve promising results. Retrieval in concept-segment level has a 14% improvement upon the concept-level. Structured query formulation improves around 13% compared with the simple logical AND formulation. The learning and retrieval process only takes 300ms, satisfying the real-time interactive search need.