The LIMSI Broadcast News transcription system
Speech Communication - Special issue on automatic transcription of broadcast news data
Learning query-class dependent weights in automatic video retrieval
Proceedings of the 12th annual ACM international conference on Multimedia
Solving multiclass learning problems via error-correcting output codes
Journal of Artificial Intelligence Research
Using information content to evaluate semantic similarity in a taxonomy
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Learning the semantics of multimedia queries and concepts from a small number of examples
Proceedings of the 13th annual ACM international conference on Multimedia
Probabilistic latent query analysis for combining multiple retrieval sources
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Video search reranking via information bottleneck principle
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
A review of text and image retrieval approaches for broadcast news video
Information Retrieval
Video search re-ranking via multi-graph propagation
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
CuZero: embracing the frontier of interactive visual search for informed users
MIR '08 Proceedings of the 1st ACM international conference on Multimedia information retrieval
Foundations and Trends in Information Retrieval
Improving Automatic Video Retrieval with Semantic Concept Detection
SCIA '09 Proceedings of the 16th Scandinavian Conference on Image Analysis
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Adaptive Learning for Multimodal Fusion in Video Search
PCM '09 Proceedings of the 10th Pacific Rim Conference on Multimedia: Advances in Multimedia Information Processing
Properties of optimally weighted data fusion in CBMIR
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
The e-recall environment for cloud based mobile rich media data management
Proceedings of the 2010 ACM multimedia workshop on Mobile cloud media computing
MQSS: multimodal query suggestion and searching for video search
Multimedia Tools and Applications
Query sampling for learning data fusion
Proceedings of the 20th ACM international conference on Information and knowledge management
Document dependent fusion in multimodal music retrieval
MM '11 Proceedings of the 19th ACM international conference on Multimedia
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
Joint-rerank: a novel method for image search reranking
Proceedings of the 2nd ACM International Conference on Multimedia Retrieval
Memory recall based video search: Finding videos you have seen before based on your memory
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
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We develop a framework for the automatic discovery of query classes for query-class-dependent search models in multimodal retrieval. The framework automatically discovers useful query classes by clustering queries in a training set according to the performance of various unimodal search methods, yielding classes of queries which have similar fusion strategies for the combination of unimodal components for multimodal search. We further combine these performance features with the semantic features of the queries during clustering in order to make discovered classes meaningful. The inclusion of the semantic space also makes it possible to choose the correct class for new, unseen queries, which have unknown performance space features. We evaluate the system against the TRECVID 2004 automatic video search task and find that the automatically discovered query classes give an improvement of 18% in MAP over hand-defined query classes used in previous works. We also find that some hand-defined query classes, such as "Named Person" and "Sports" do, indeed, have similarities in search method performance and are useful for query-class-dependent multimodal search, while other hand-defined classes, such as "Named Object" and "General Object" do not have consistent search method performance and should be split apart or replaced with other classes. The proposed framework is general and can be applied to any new domain without expert domain knowledge.