Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Verbs semantics and lexical selection
ACL '94 Proceedings of the 32nd annual meeting on Association for Computational Linguistics
MIR '06 Proceedings of the 8th ACM international workshop on Multimedia information retrieval
International Journal of Computer Vision
The Google Similarity Distance
IEEE Transactions on Knowledge and Data Engineering
Proceedings of the 18th international conference on World wide web
Semantic context transfer across heterogeneous sources for domain adaptive video search
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Inferring semantic concepts from community-contributed images and noisy tags
MM '09 Proceedings of the 17th ACM international conference on Multimedia
NUS-WIDE: a real-world web image database from National University of Singapore
Proceedings of the ACM International Conference on Image and Video Retrieval
Known-item video search via query-to-modality mapping
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Short communication: Towards a universal detector by mining concepts with small semantic gaps
Expert Systems with Applications: An International Journal
Multimedia event detection with multimodal feature fusion and temporal concept localization
Machine Vision and Applications
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Can we have a universal detector that could recognize unseen objects with no training exemplars available? Such a detector is so desirable, as there are hundreds of thousands of object concepts in human vocabulary but few available labeled image examples. In this study, we attempt to build such a universal detector to predict concepts in the absence of training data. First, by considering both semantic relatedness and visual variance, we mine a set of realistic small-semantic-gap (SSG) concepts from a large-scale image corpus. Detectors of these concepts can deliver reasonably satisfactory recognition accuracies. From these distinctive visual models, we then leverage the semantic ontology knowledge and co-occurrence statistics of concepts to extend visual recognition to unseen concepts. To the best of our knowledge, this work presents the first research attempting to substantiate the semantic gap measuring of a large amount of concepts and leverage visually learnable concepts to predicate those with no training images available. Testings on NUS-WIDE dataset demonstrate that the selected concepts with small semantic gaps can be well modeled and the prediction of unseen concepts delivers promising results with comparable accuracy to preliminary training-based methods.