A web-based kernel function for measuring the similarity of short text snippets
Proceedings of the 15th international conference on World Wide Web
Large-Scale Concept Ontology for Multimedia
IEEE MultiMedia
A reranking approach for context-based concept fusion in video indexing and retrieval
Proceedings of the 6th ACM international conference on Image and video retrieval
Video search in concept subspace: a text-like paradigm
Proceedings of the 6th ACM international conference on Image and video retrieval
Semantic concept-based query expansion and re-ranking for multimedia retrieval
Proceedings of the 15th international conference on Multimedia
Extended gloss overlaps as a measure of semantic relatedness
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
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
Adaptive Learning for Multimodal Fusion in Video Search
PCM '09 Proceedings of the 10th Pacific Rim Conference on Multimedia: Advances in Multimedia Information Processing
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In light of the strong demands for semantic search over large-scale consumer photos, which generally lack reliable user-provided annotations, we investigate the feasibility and challenges entailed by the new paradigm, concept search - retrieving visual objects by large-scale automatic concept detectors with keywords. We investigate the problem in three folds: (1) the effective concept mapping and selection methods over large-scale concept ontology; (2) the quality and feasibility of the pre-trained concept detectors applying on cross-domain consumer data (i.e., Flickr photos); (3) the search quality by fusing automatic concepts and user-annotated data (tags). Through experiments over large-scale benchmarks, TRECVID and Flickr550, we confirm the effectiveness of concept search in the proposed framework, where the semantic mapping by web-based kernel function over Google snippets significantly outperforms conventional WordNet-like methods both in accuracy and efficiency.